Methods, apparatus, systems, and articles of manufacture are disclosed. An apparatus for executing a rule includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to access a property value from a data collector, the property value including an operational value of a workstation within a process control system, create a data model instance representing the workstation, apply the property value to the data model instance, identify a rule associated with the data model instance, cause execution of an executable package associated with the rule using the data model instance; and record a result of the execution of the executable package.
H04L 43/10 - Active monitoring, e.g. heartbeat, ping or trace-route
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
Methods, apparatus, systems, and articles of manufacture are disclosed. An example system to modify an industrial control system includes: at least one memory; programmable circuitry; and instructions to cause the programmable circuitry to: configure a device driver based on a first command, the first command to configure the device driver to initiate a device-specific communication protocol to collect input data from a publisher device coupled to the device driver; access a second command from a subscriber device, the second command to include a device identifier of the publisher device and to specify at least one of a communication mode, a device calibration configuration, or a fault detection configuration, the second command based on a product quality prediction, the product quality prediction generated using a spectral data model; and provide the second command to the device driver.
Methods, apparatus, systems, and articles of manufacture are disclosed for an application marketplace for process control systems. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to detect at least one of a configuration or a state of operation of a process control system based on telemetry data associated with the process control system, execute a machine learning model to generate an output based on the at least one of the configuration or the state of operation, the output to be representative of a recommendation to change a portion of the process control system, and cause a change of the portion of the process control system based on the recommendation.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
Methods, apparatus, systems, and articles of manufacture are disclosed for sequence of event generation for a process control system. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of execute or instantiate the machine readable instructions to obtain a first digital signal from a first field device representative of a first sensor data value labeled with a first timestamp generated by the first field device, obtain a second digital signal from a second field device representative of a second sensor data value labeled with a second timestamp generated by the second field device, and store a data association of the first and second sensor data values in a datastore, the data association representative of a sequence of events including an ordering of the first sensor data value and the second sensor data value based on the first and second timestamps.
G05B 19/04 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
G05B 19/414 - Structure of the control system, e.g. common controller or multiprocessor systems, interface to servo, programmable interface controller
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
G16Y 40/35 - Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually-exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually- exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
7.
MONITORING AND OPERATIONAL FUNCTIONALITIES FOR AN ENTERPRISE USING PROCESS CONTROL OR AUTOMATION SYSTEM
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually-exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
8.
EMBEDDED DEVICE IDENTIFICATION IN PROCESS CONTROL DEVICES
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually- exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
9.
SECURING ACCESS OF A PROCESS CONTROL OR AUTOMATION SYSTEM
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually-exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
10.
SECURING CONNECTIONS OF A PROCESS CONTROL OR AUTOMATION SYSTEM
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually-exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
11.
MANAGEMENT FUNCTIONALITIES AND OPERATIONS FOR PROVIDER OF PROCESS CONTROL OR AUTOMATION SYSTEM
A process plant and industrial control system architecture includes a generalized compute fabric that is agnostic or indifferent to the physical location at which the compute fabric is implemented, includes one or more physical control or field devices located at one or more specific sites at which a product or process is being manufactured and further includes a transport network that securely provides communications between the compute fabric and the pool of physical devices. The compute fabric includes an application layer that includes configured containers or containerized software modules that perform various control, monitoring and configuration activities with respect to one or more devices, control strategies and control loops, sites, plants, or facilities at which control is performed, and includes a physical layer including computer processing and data storage equipment that can be located at any desired location, including at or near a site, plant, or facility at which control is being performed, at a dedicated location away from the location at which control is being performed, in re-assignable computer equipment provided in the cloud, or any combination thereof. This control architecture enables significant amounts of both computer processing and IT infrastructure that is used to support a process plant, an industrial control facility or other automation facility to be implemented in a shared, in an offsite and/or in a virtualized manner that alleviates many of the communications and security issues present in current process and industrial control systems that attempt to implement control with shared or virtualized computing resources set up according to the well-known Purdue model. The industrial control system architecture is protected via more secure and customizable techniques as compared to those used in Purdue model-based control systems. For example, communications between any (and in some cases, all) endpoints of the system may be protected via one or more virtual private networks to which authenticated endpoints must be authorized to access. Endpoints may include, for example, containerized components, physical components, devices, sites or locations, the compute fabric, and the like, and the VPNs may include mutually- exclusive and/or nested VPNs. External applications and services, whether automated or executing under the purview of a person, may access information and services provided by the system via only APIs, and different sets of APIs may be exposed to different users that have been authenticated and authorized to access respective sets of APIs. A configuration system operates within the compute fabric to enable a user to easily make configuration changes to the compute fabric as the user does not generally need to specify the computer hardware within the compute fabric to use to make the configuration changes, making it possible for the user to deploy new configuration elements with simple programming steps, and in some cases with the push of a button.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
12.
METHODS AND APPARATUS TO GENERATE AND DISPLAY TRENDS ASSOCIATED WITH A PROCESS CONTROL SYSTEM
Methods and apparatus to generate and display trends associated with a process control system are disclosed. An example apparatus includes memory, machine readable instructions, and processor circuitry to execute the instructions to generate a first graphical user interface. The first graphical user interface to include a graphical representation of a component in a process control system. The processor circuitry to generate a second graphical user interface. The second graphical user interface to include a chart region with a trend represented therein. The trend indicative of values of a process parameter of the process control system over a period of time. The processor circuitry to automatically generate the trend in the chart region in response to a graphical element being dragged and dropped from the first graphical user interface to the second graphical user interface.
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
G06Q 10/0639 - Performance analysis of employees; Performance analysis of enterprise or organisation operations
Process knowledge creation, development, and management techniques allow for and enable the creation of a universal process definition (UPD) of an industrial process, the automatic conversion or transformation of the UPD into different site-specific process definitions, and the implementation of the site-specific process definitions at different manufacturing, production, and/or automation sites. Typically, the UPD is site- and equipment- agnostic, and the transformation may generate and provide a set of site-specific process definition implementation files or routines to configure and/or govern the behavior of various site-specific execution systems, e.g., as site-specific operational instances of the UPD. The techniques may utilize feedback and information generated by site-specific operational instances to generate learned knowledge and update the UPD accordingly so that subsequent instantiations of the UPD may incorporate (and reap the benefits of) the learned knowledge. The techniques may automatically select a most suitable site for a particular instantiation of the UPD.
A real-time control system includes a fault detection training technique to implement a data-driven fault detection function that provides an operator with information that enables a higher level of situational awareness of the current and likely future operating conditions of the process plant. The fault detection training technique enables an operator to recognize when a process plant component is behaving abnormally to potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a stall of the process control system or a failure of the process plant component.
A system for securely and efficiently obtaining data from a process plant and processing that data for consumption by one or more external applications or systems includes receiving event data from various data sources in or associated with a plant via various different data formats and data communication structures at a centralized server or gateway, striping off the communication format structure from the data, placing the data, including metadata associated with the data, into an event stream, and making the data in the event stream available to a processing infrastructure that processes that data in a comprehensive and robust manner for easy consumption by external data mining, data visualization and data analytic systems or applications.
A quality review management system may be used to analyze the operation of manufacturing processes within a plant based on data collected by various data sources in the plant, such as batch executive applications, to automatically detect, store, and display exceptions within those processes for use by a quality review engineer to determine if the process operation meets certain quality standards. The quality review management system includes a configuration application that enables a user to create one or more exception rules, an exception engine that analyses process data using the rules to detect one or more exceptions within the process, and a review application that enables quality review personnel to review each determined exception for resolution purposes.
In a method of providing virtual enhanced vision to a user of an augmented reality (AR) mobile device, it is determined that a first node associated with a map of a process control environment corresponds to a first real- world object currently within a field of view of a camera of the AR mobile device. A relationship between the first node and one or more other nodes is determined, with the relationship indicating that one or more other objects corresponding to other nodes are at least partially obscured by the first object. At least partially in response to determining the relationship, one or more digital models or images depicting the other object(s) is/are retrieved from memory. A display of the AR mobile device is caused to present the retrieved digital models or images to the user while the first object is in the field of view of the camera.
In a method of facilitating interaction between a user of an augmented reality (AR) mobile device and a first real- world object, a display device is caused to superimpose digital information on portions of a process control environment within a field of view of a camera of the device. The superimposed information is associated with nodes in a map of the environment, and the nodes correspond to other objects in the environment. The display is caused to indicate a direction to the first object. After detecting a user input that indicates selection of the first object, the display is caused to superimpose, on a portion of the process control environment currently within the field of view, a digital model or image of the first object. A user interface is caused to provide one or more virtual controls and/or one or more displays associated with the first object.
An industrial control system, such as a process control for use in a process plant, uses a hardware/software architecture that makes the system more reactive by making the system more resilient, responsive, and elastic. The industrial control system includes one or more distributed input/output (I/O) controller devices (BFN I/O controllers) which are coupled to field devices within a plant and provide direct or indirect access to the field devices for control and messaging purposes, one or more advanced function and computation nodes, and one or more user nodes coupled to the BFN I/O controllers via a network connection. The advanced function nodes store and execute virtual machines, devices, or entities, which decouples the hardware used in the control system from the software that functions on that hardware, making the system easier to scale, reconfigure, and change. Moreover, the industrial control system uses a self-describing data messaging scheme that provides both the data and a description of the data from a sender to a receiver, which enables different messaging protocols and data formats to be used in the control system, which also makes the system more open.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
20.
MULTI-PROTOCOL FIELD DEVICE IN PROCESS CONTROL SYSTEMS
A field device includes components to communicate with a control and/or asset management system of a process control system or with other field devices using any of several different communication protocols such as several different internet protocol (IP) protocols. This architecture allows for a single version of a field device to be provided in automation or plant control systems that use any of these communication protocols, thus saving on inventory and product development costs. Moreover, the multi-protocol field device or a system using the multi-protocol field device can manage the asset (read and write parameterized data from and to the asset) using one protocol while at the same time communicating real-time process/factory automation information using a second and different protocol. Moreover, the field device may be able to communicate to other devices including other field devices and host devices using both of these protocols or other protocols for different purposes.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/06 - Communication control; Communication processing characterised by a protocol
An I/O-abstracted configuration is defined for a field device that has not yet been assigned or allocated to communicate via a particular I/O device, and the field device (and optionally portions of the process control loop of which the field device is a part) is commissioned based on contents of its I/O-abstracted configuration. The field device's I/O-abstracted configuration is stored in an instance of a device placeholder object, which may be common to multiple types of devices and multiple types of I/O. A property of the device placeholder object may be exposed based on the value entered for another property, and the device placeholder object may store abstracted values as well as explicit or discrete values that are descriptive of the field device and its behavior. Upon I/O-assignment or allocation, values held in the device's I/O-abstracted configuration may be transferred to or otherwise synchronized with the device's as-built configuration.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
22.
CONFIGURATION IN PROCESS PLANT USING I/O-ABSTRACTED FIELD DEVICE CONFIGURATIONS
An I/O-abstracted configuration is defined for a field device that has not yet been assigned or allocated to communicate via a particular I/O device or I/O network within a plant, and this configuration is stored in a device placeholder object in a back-end environment of the plant. Thereafter other objects, modules, applications, user interfaces, etc., that are to execute in the back-end environment of the plant to communicate with the field device during on-line operation of the plant may be designed, built, configured, and tested using the device placeholder object without any actual communications with the field device and without assigning the device placeholder object to a particular I/O channel or I/O network. A commissioning system which may create and store one or more device placeholder objects in a database within the back-end environment of the plant includes an execution engine that executes one or more other back-end environment objects to be commissioned and tested, and a communication interface that determines, from the device placeholder object if a field device is in an I/O-unallocated device state. If so, the communication interface uses the configuration data stored in the device placeholder object to verify that the form, format, and configuration of the object being tested is correct to properly communicate with the field device. Moreover, a simulation engine can generate simulated device signals to enable further testing of the object.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
23.
BINDING OF DEVICES IN A PROCESS PLANT USING I/O-ABSTRACTED FIELD DEVICE CONFIGURATIONS
During commissioning activities of a process plant, a device placeholder object that stores an I/O-abstracted configuration of a particular field device within the plant is created and stored in a device in the back-end environment of the plant and a further configuration file is stored in or for the particular field device in the field equipment environment of the plant. The device placeholder object, which will eventually be associated with the particular field device, and the field device configuration file are used to perform separate commissioning activities in each of these plant environments before the field devices are configured to communicate with a process controller via a particular I/O network within the plant. Thereafter, a binding application performs a discovery process to detect the I/O communication path through which each field device is connected to the back-end environment. The discovery process traverses through the I/O network as built, and autosenses the devices within the I/O network until this process discovers a device placeholder object or a configuration file for a particular field device. The binding application then determines if the information within these two device configuration files match, and if so, binds the field device to the back-end by storing the detected communication path for the particular device in a configuration database, such as in the device placeholder object for the particular field device. If the configuration information in the device placeholder object does not match the configuration file for a discovered field device, the binding application may perform a reconciliation procedure to determine the correct configuration information for the particular field device.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
24.
DETERMINING DEVICE SYSTEM TAGS FOR COMMISSIONING PORTIONS OF A DISCONNECTED PROCESS CONTROL LOOP
A system tag identifying a device of a process control loop is determined/derived from a unique identifier of the device while the loop is communicatively disconnected from a process plant's back-end environment or control room. The system tag may be stored at the device itself or at a proxy that is disposed in the field of the process plant (e.g., at another component of the loop). One or more commissioning activities that include the device are performed in the field using the device's system tag, and at least some of the field commissioning activities may be automatically triggered based on the derivation of system tag. Upon the loop being communicatively connected to the back-end environment of the plant, the device's system tag known to the loop in the field is synchronized with the device's system tag known to the back-end of the process plant.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
Techniques for automatically testing an entire process control loop, such as after components and portions of the loop have been commissioned separately, or after run-time operation begins, enable the process control loop to be tested without an operator in a back-end environment of a process plant coordinating with an operator in a field environment of the process plant to supply inputs and/or generate various conditions at the loop. Instead, a single operator performs a single operation to initiate an automatic loop test, or in some implementations, no user input is needed to initiate and/or perform the automatic loop test. Automatic loop testing includes automatically causing a field device to operate in a plurality of test states and determining whether resultant loop behaviors are expected behaviors. Multiple loops may be tested concurrently or distinct in time. An automatic loop test result is generated and may be presented via a user interface.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
Disclosed herein are techniques for automatically distributing device identification amongst components of a process control loop in a process plant while the loop is communicatively disconnected from the plant's back-end environment or control room. A field device's identification is stored in a memory of a component of the loop (which may be the field device or a proxy) and is used for commissioning the field device. While the loop remains disconnected, the field device's identification is distributed to the memory of another component of the loop and used for commissioning a portion of the loop including the field device and the another component. Additional distribution to other components for commissioning other loop portions is possible. Distribution may be triggered by the completion of certain commissioning activities, the establishment of communicative connections between components, and/or other conditions. Other parameters and/or information descriptive of the field device may be similarly distributed.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
27.
SYSTEM AND METHOD FOR VERIFYING THE SAFETY LOGIC OF A CAUSE AND EFFECT MATRIX
A system and method for determining a configuration of a process control system for a process plant, the process control system implemented as a set of function blocks, includes, for each of the set of function blocks, determining a configuration of the function block based on: (i) a set of outputs of the function block, (ii) logic of the function block, and (iii) a set of inputs of the function block. The system and method further includes generating, based on the set of configurations of the set of function blocks, a test cause and effect matrix (CEM) having a set of test causes and a set of test effects. The system and method further includes accessing a requirement-defining CEM having a set of causes and a set of effects. The system and method may also include comparing the test CEM to the requirement-defining CEM to determine whether a set of discrepancies exists.
A system and method for defining a cause and effect matrix (CEM) within a process control system for a process plant, including accessing a cause and effect matrix (CEM) having a set of causes and a set of effects, wherein each of the set of causes represents a condition within the process plant and each of the set of effects represents an effect to be performed within the process plant. The system and method further includes, for each of the set of effects: (i) identifying a subset of the set of causes according to a corresponding set of the cause-effect pairs corresponding to the effect of the set of effects, (ii) defining the subset of the set of causes as a single-dimension matrix, and (iii) automatically calculating a numerical representation for the single-dimension matrix. The system and method further includes configuring a set of function blocks for the process control system according to the set of numerical representations.
A system and method of configuring monitor blocks and effect blocks associated with a process control system for a process plant includes causing a display device to display a graphical user interface, the graphical user interface indicating a first monitor block, a second monitor block, and an effect block. The system and method further includes enabling a user to input configuration data via the input device, including: (i) configuring one of the outputs of the first monitor block to serve as one of the inputs of the second monitor block, (ii) configuring an additional one of the outputs of the first monitor block and one of the outputs of the second monitor block to serve as inputs to the effect block, and (iii) designating at least one of the plurality of cells of each of the first monitor block, the second monitor block, and the effect block as a trigger associated with the respective input/output pair for the respective cell and corresponding to a condition in the process plant.
A system and method for managing function blocks within a process control system for a process plant includes accessing an initial cause and effect matrix (CEM) having a set of causes and a set of effects. The system and method may then define a set of related groups within the initial CEM including: (i) accessing a set of rules associated with the set of related groups, (ii) identifying a portion of the set of causes that are related to a portion of the set of effects according to the set of rules and based on at least a portion of the corresponding cause-effect pairs, and (iii) rearranging the portion of the set of causes and the portion of the set of effects such that the portion of the corresponding cause-effect pairs are rearranged.
A system and method for enabling access to information included in a safety requirement specification (SRS) for a process plant, the process plant controlled by a process control system including displaying, in a user interface, a cause and effect matrix (CEM) having a set of elements including a set of causes and a set of effects, wherein each of the set of causes represents a condition within the process plant and each of the set of effects represents an effect to be performed within the process plant, and wherein at least some of the set of causes and the set of effects are related as cause-effect pairs whereby the corresponding effect activates in response to an occurrence of the corresponding condition. The system and method further including receiving, via the user interface, a selection of an element of the set of elements, and, in response to receiving the selection: (i) accessing, from the SRS, a set of information associated with the element of the set of elements, and (ii) displaying the set of information in the user interface.
A system and method for visualizing safety events within a process plant includes accessing a cause and effect matrix (CEM) having a set of causes and a set of effects, wherein each of the set of causes represents a condition within the process plant and each of the set of effects represents an effect to be performed within the process plant, wherein the set of causes and the set of effects are representative of a set of monitored safety events within the process plant. The system and method further includes receiving a selection of a monitored safety event of the set of monitored safety events. The system and method may also include displaying, in a user interface, (i) an indication of the monitored safety event, and (ii) a current status of the monitored safety event. The system and method may also include detecting a change in status to the monitored safety event. Further, the system and method may include displaying, in the user interface, an updated status of the monitored safety event according to the change in status.
A data pipeline is used as a fundamental processing element for implementing techniques that automatically or autonomously perform signal processing-based learning in a process plant or monitoring system. Each data pipeline includes a set of communicatively interconnected data processing blocks that perform processing on one or more sources of data in a predetermined order to, for example, clean the data, filter the data, select data for further processing, perform supervised or unsupervised learning on the data, etc. The individual processing blocks or modules within a data pipeline may be stored and executed at different devices in a plant network to perform distributed data processing. Moreover, each data pipeline can be integrated into one or more higher level analytic modules that perform higher level analytics, such as quality prediction, fault detection, etc. on the processed data. The use of data pipelines within a plant network enables data collected within a plant control or monitoring system to be processed automatically and used in various higher level analytic modules within the plant during ongoing operation of the plant.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
34.
MODEL PREDICTIVE CONTROL USING WIRELESS PROCESS SIGNALS
A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
A control technique controls a process in a manner that reduces the number of controller changes provided to a controlled device, and so reduces the power consumption of the controlled device along with the loading of a process control communications network disposed between the controller and the controlled device. This technique is very useful in a control system having wirelessly connected field devices, such as sensors and valves which, in many cases, operate off of battery power. Moreover, the control technique is useful in implementing a control system in which control signals are subject to intermittent, non- synchronized or significantly delayed communications and/or in a control system that receives intermittent, non- synchronized or significantly delayed process variable measurements to be used as feedback signals in the performance of closed-loop control.
G05B 11/42 - Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
A distributed big data device in a process plant includes an embedded big data appliance configured to locally stream and store, as big data, data that is generated, received, or observed by the device, and to perform one or more learning analyses on at least a portion of the stored data. The embedded big data appliance generates or creates learned knowledge based on a result of the learning analysis, which the device may use to modify its operation to control a process in real-time in the process plant, and/or which the device may transmit to other devices in the process plant. The distributed big data device may be a field device, a controller, an input/output device, or other process plant device, and may utilize learned knowledge created by other devices when performing its learning analysis.
Systems and methods for automated commissioning of virtualized distributed control systems are disclosed. An example method includes accessing a data structure including a list of configuration names for network cards associated with first and second host servers of a virtual process control environment. The first and second host servers implement virtual machines corresponding to workstations for a process control system. The example method also includes when configuring the first host server, assigning a first name to a first one of the network cards associated with the first host server. The example method further includes when configuring the second host server, assigning the first name to a second one of the network cards associated with the second host server based on a user selection of the first name from the list of configuration names. The second host server is configured after the first host server.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
38.
CHANGE MANAGEMENT SYSTEM IN A PROCESS CONTROL ARCHITECTURE
A computer-implemented system and method (300) of managing changes to a process control system (10) are provided. The method (300) includes obtaining a plurality of changes (304) to the process control system (10). The plurality of changes (304) are categorized into a plurality of categories (104,106,108). Each change is assigned an initial status (314). The categorized changes are displayed with their associated status to a user to receive user action relative to at least one categorized change. A status of the at least one categorized change is stored.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
A computer-implemented method (240) for configuring a plurality of field devices is provided. The method (240) includes defining (242) a configuration template and mapping (244) the configuration template to a plurality of field devices. The defined configuration template is automatically applied to the plurality of field devices. A method (300) of verifying field device configuration information is also provided.
A process control monitoring system for a process control plant uses graphic trend symbols to assist in detecting and monitoring trends of process variables within the process control plant. A graphic display application within the process control monitoring system may implement and display each graphic trend symbol to graphically indicate or encapsulate current trend and value information of a process variable within the process control plant. The graphic display application may display the graphic trend symbol in a spatially realistic location within a graphical representation of the process control plant while maintaining the hierarchical structure or each hierarchical level of the process plant. The graphic display application may also include a zoom feature that enables a user to quickly drill down through tend data to obtain more information and to support problem identification and diagnosis tasks.
A process control monitoring method uses a navigation pane for navigating within a graphical depiction of a process control plant. The navigation pane that includes a unit selection area and an equipment selection area, wherein (i) the unit selection area includes one or more unit selector icons that correspond to one or more units in the graphical depiction of the process control plant, (ii) the equipment selection area includes one or more equipment selector icons that correspond to one or more equipment in the graphical depiction of the process control plant, (iii) each unit in the process control plant includes one or more equipment in the process control plant, and (iv)each unit selector icon is associated with one or more equipment items selector icons based on the included one or more equipment in the process control plant associated with the unit that corresponds to the unit selector icon. In response to receiving a selection of a first unit selector icon, one or more equipment selector icons in the equipment area associated with a first unit in the process control plant that corresponds to the selected first unit selector icon are displayed, wherein (i) the one or more equipment selector icons associated with the selected first unit selector icon are displayed in the equipment selection area and (ii) the one or more non-selected unit selector icons remain displayed in the unit selection area. Moreover, one or more equipment in the graphical depiction of the process control corresponding to the first unit selector icon is displayed.
A process control monitoring system for a process control plant uses graphic trend symbols to assist in detecting and monitoring trends of process variables within the process control plant. A graphic display application within the process control monitoring system may implement and display each graphic trend symbol to graphically indicate or encapsulate current trend and value information of a process variable within the process control plant. The graphic display application may display the graphic trend symbol in a spatially realistic location within a graphical representation of the process control plant while maintaining the hierarchical structure or each hierarchical level of the process plant. The graphic display application may also include a zoom feature that enables a user to quickly drill down through tend data to obtain more information and to support problem identification and diagnosis tasks.
A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.
Systems and methods to graphically display process control system information are disclosed. Some example methods include monitoring process variables in a process control system, determining a current state of a first one of the process variables, and determining a trend associated with the first process variable. Some such examples further include generating a first graphic representative of information associated with the first process variable, the information comprising the current state of the first process variable and the trend of the first process variable. Some example methods also include rendering the first graphic via a display.
A display configuration system enables plant operators to create their own process displays called dashboards during run-time of the plant and in the same interface that these operators use to view operation of the process plant. This display configuration system makes the operators more productive because the operators can quickly create and implement their own specialized dashboards, as these operators determine these dashboards are needed. Each dashboard has a defined layout specifying locations or regions at which display elements can be shown in the dashboard, and this layout is operator modifiable. Operators can easily create content on their own dashboards using predefined but configurable display building blocks called gadgets, which can be pre-stored in a library and can be made available for the operator during dashboard creation activities. A gadget can be dragged and dropped onto a dashboard at one of the regions or locations of the dashboard to be installed in that region or location of the dashboard. The display configuration system may automatically size the gadgets based on the selected dashboard layout, and operators can modify an existing dashboard by adding, modifying, moving, minimizing or deleting gadgets on the dashboard.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 9/44 - Arrangements for executing specific programs
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
Techniques for flexibly configuring an operating process plant or process control system enable a change to a parent object to be selectively propagated to child objects derived from the parent object, so that a first subset of child objects and their respective instantiations are updated with the change, while a second subset and their respective instantiations remain unchanged. The parent object may be a class or instance process object corresponding to a process entity, and the first and/or the second subset of child objects may be selected. In some cases, the change may have been a propagated change, to the parent object, from a child object that is excluded from the first or the second subset of child objects. In some cases, the change may first be propagated to an instance object derived from the parent object, and then propagated from the instance object to a child object. Flexible configuration of process control systems or plants allows draft changes or modifications to be made to parent process objects, e.g., in a configuration environment, without automatically triggering corresponding instantiations and/or downloads of the parent process objects and/or their derived children objects into a run-time system. Parent objects to which draft changes are allowed may include class objects, instance objects, and/or library objects. One or more modifications to a process object may be saved as a draft, and multiple drafts for a same process object may be saved as different versions. Children objects may indicate the particular version of a parent object draft from which they are derived. A user may indicate that a particular draft or version is to be published or approved. Unpublished or unapproved drafts are prevented from being instantiated in the run-time system, whereas published or approved drafts are allowed to be instantiated. Flexible graphic element objects in a process plant are configurable both in a run-time operating environment in which a process is being controlled and in a configuration environment. An instantiated flexible graphic element object may be a display view or may be another graphic element included on a display view. A graphic element object may be linked to and/or derived from another graphic element object, and changes to a particular graphic element object may be propagated to its derivations, e.g., according to a distribution policy. Changes to definitions corresponding to a particular graphic element object (e.g., to the definition of a graphic element attribute such as a shape, animation, event handler or property) may be overridden or modified in another object derived from the particular graphic element object. The modified derived object may be renamed and saved separately from the particular graphic element object.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G06F 9/44 - Arrangements for executing specific programs
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
Generating a maintenance route in a process control system includes creating an initial ordered list of all wireless nodes in direct communication with a wireless gateway, where the nodes are ordered by signal strength with the wireless gateway device. A subsequent ordered list is created of all nodes in direct communication with first node of the initial ordered list, where the nodes are ordered by signal strength with the first node. The subsequent ordered list is then appended to the initial ordered list after the first node. This process of creating a subsequent list and appending the initial list is iteratively repeated thereafter, each time accounting for the next node in the appended ordered list following the previous iteration until all nodes are accounted for. In the last iteration, the nodes correspond to stop points along the route and the order corresponds to the route to be taken among the stop points.
A communication method operates to seamlessly transmit internet protocol (IP) data frames, such as IPv6 data frames, over a communication network that uses a non-IP network routing protocol, i.e., a communication network that implements a network routing protocol other than, or that is incompatible with an IP network routing protocol, such as the WirelessHART protocol. This communication method enables, for example, field devices or other intelligent devices within a process plant network that uses a non-IP communication network (a network that does not use IP based network routing) to perform messaging of IP data frames generated at or to be received by internet protocol enabled devices either within the process plant network or outside of the process plant network. The communication method does not affect or alter the normal communications within the non-IP communication network because the communication method uses the network routing structure of the non-IP communication network to transmit the IP data frames within the non-IP communication network, while preserving the IP network routing information of the IP data frames needed to subsequently route the messages in an IP based communications network or to decode and use IP message data frames in an IP enabled device.
A method (150) of interacting with a process control system is provided. The method (150) includes bringing a mobile electronic device into physical proximity of a field device (152). The mobile electronic device is coupled to a digital process communication channel of the field device (154). A client software application is initiated on the mobile electronic device (156). The digital process communication channel is used to communicatively couple the client software application to a host application remote from both the mobile electronic device and the field device (158).
Example methods and apparatus to manage process control resources are disclosed. A disclosed example method includes receiving a selection of a first process control resource within a process control system to be associated with a logical container, the logical container including other process control resources that have a same user defined characteristic in common with the first process control resource, creating an entry within the logical container for the first process control resource by storing an identifier of the first process control resource in the logical container, and assigning the first process control resource to the logical container so that the identifier of the first process control resource links to process control information associated with the first process control resource.
Example methods and apparatus to transmit device description files to a host are disclosed. A disclosed example method includes communicatively coupling a field device to the host to provision the field device within a process control system, receiving an indication that the host does not include a version of a device description file that corresponds to a version of the field device, accessing the device description file from a memory of the field device, and transmitting the device description file from the field device to the host.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
52.
METHODS AND APPARATUS TO VIRTUALIZE A PROCESS CONTROL SYSTEM
Methods and apparatus to virtualize a process control system are described. A described process control system includes a server cluster including one or more servers. When operating, the server cluster provides a virtual workstation or virtual server, a virtual controller to interoperate with the virtual workstation or server and to implement process control operations, and a virtual input/output device to interoperate with the virtual controller and coupled to one or more field devices within the process control system.
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
53.
METHODS AND APPARATUS TO DISPLAY PROCESS CONTROL DEVICE INFORMATION
Example methods and apparatus to display process control information are disclosed. A disclosed example method includes receiving a conditional device parameter in a processor from a process control device, determining if functionality associated with the process control device is available, wherein the conditional device parameter indicates if the functionality is available, and displaying the conditional device parameter within a graphic via a user interface if the functionality is available.
Example methods and apparatus to display process control information are disclosed. A disclosed example method includes receiving in a processor a first status of a first mode element, the first mode element indicating a current operating condition of an object of a process control device, receiving in the processor a second status of a second mode element, the second mode element indicating a desired operating condition of the object of the process control device, determining a mode of the object based on the first status and the second status, the mode indicating a condition of the object of the process control device, and displaying the mode of the object of the process control device within a user interface as a graphic.
A handheld field maintenance tool (52, 102) with improved diagnostic functions is provided. The tool (52, 102) includes a process communication module (121, 138) configured to interact with a field device (22, 23, 104). A controller (130) is coupled to the process communication module (121, 138). The controller (130) is configured to execute a number of improved diagnostic functions relative to the field device (22, 23, 104). The controller (130) may obtain contextual information (206) relative to a current field maintenance operation and preload at least one resource relative to a next field operation step (208). The controller (130) may obtain process alarm information (254) through a wireless communication module (123), and field device alert information (254) through the process communication module (121, 138) and provide an indication on a display (120) relative to both process alarm information and field device alert information. The controller (130) may execute a sequence of field device maintenance operations on the field device in response to a signal from a user input device (122). The controller (130) may obtain snapshot information (304) in response to a signal from a user input device (122).
A handheld field maintenance tool (52, 102) is provided. The tool (52, 102) includes, among other things, a wireless process communication protocol module (121) configured to communicate in accordance with a wireless process communication protocol. The tool (52, 102) also includes a display (120) and an input device (122). A controller (130) is coupled to the wireless process communication protocol module (121), the display (120), and the input device (122). The controller (130) is configured to generate a map on the display (120) indicating a position of the handheld field maintenance device (52, 102) relative to at least one asset, such as a field device (22, 23, 104). The controller (130) is further configured to determine a position of the handheld field maintenance device (52, 102) by triangulating using wireless process communication with a number of known, fixed-position wireless field devices (104).
A handheld field maintenance tool (52, 102) is provided. The handheld field maintenance tool (52, 102) includes a process communication module (121, 138) configured to communicate with a field device (22, 23, 104). The handheld field maintenance tool (52, 102) also includes a display (120) and a user input device (122). A controller (130) is coupled to the process communication module (121, 138), the user input device (122) and the display (122) and is configured to generate a listing of task- based field maintenance operations on the display (120) and receive a user input selecting a task-based field maintenance operation (254). The controller (130) is configured to automatically traverse (258) a menu of the field device (22, 23, 102) using a fast-key sequence relative to the selected task. A method of creating a task-based field maintenance operation (260) is provided. A method of interacting with a field device menu (300) is also provided.
An intrinsically-safe handheld field maintenance tool (52, 102) is provided. The tool (52, 102) includes a process communication module (121, 138) configured to communicate with a field device (22, 23, 104) in accordance with a process industry communication protocol. A controller (130) is coupled to the process communication module (121, 138) and is configured to provide at least one function related to maintenance of the field device (22, 23, 104). Program instructions embodied on a computer readable medium coupled to the controller, the program instructions causing the controller, when executed by the controller, to provide operator rounds functionality (204), CMMS/EAM functionality (206) and/or ERP functionality (208).
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
A handheld field maintenance tool (52, 102) includes a training mode. The handheld field maintenance tool (52, 102) has a process communication module (121, 138) operably coupleable to a field device (22, 23, 104), a user interface (156), and a controller (130) coupled to the process communication module (121, 138) and the user interface (156). The controller (130) is configured to interact with a user through the user interface (156), and is configured to provide a simulation function where at least one characteristic of the field device (22, 23, 104), indicated through the user interface (156), is generated by the controller (130) instead of the field device (22, 23, 104).
A handheld field maintenance tool (52, 102) and associated method (200) are provided. The handheld field maintenance tool (52, 102) includes a process communication module (121, 138) configured to communicate in accordance with a process industry communication standard. A controller (130) is coupled to the process communication module (121, 138) and is configured to access a device description (206) relative to a selected simulated field device. A user interface (156) is configured to receive a user input relative to a parameter of the simulated field device. The controller (130) generates communication through the process communication module (121, 138) to simulate the selected field device based on the user input.
An intrinsically-safe handheld field maintenance tool (52, 102) includes a process communication module (121, 138) configured communicatively couple to a field device (22, 23, 104). A camera (157) is configured to obtain at least one image relative to the field device (22, 23, 104). A controller (130) is coupled to the process communication module (121, 138) and operably coupled to the camera (157). The controller (130) is configured to store the at least one image relative to the field device (22, 23, 104). The handheld field maintenance tool (52, 102) may also include or employ an audio input device to capture audio files.
A process is modeled by resolving the process into a plurality of process stages, including at least a first process stage and a second process stage, and developing a plurality of models, each model corresponding to a respective one of the plurality of process stages, wherein the model corresponding to each process stage is developed using data from one or more runs of that process stage and output quality data relating to the one or more runs of that process stage and wherein the model corresponding to each process stage is adapted to produce an output quality prediction associated with that process stage, and wherein the output quality prediction produced by the model of a first one of the process stages is used to develop the model of a second one of the process stages.
A method (200) of commissioning a wireless field device (50) is provided. The method (200) includes communicatively coupling (202) a handheld field maintenance tool (100) to the wireless field device (50) to obtain a wireless field device identifier (204), A wireless network is selected. Wireless communication is generated between the handheld field maintenance tool (100) and a wireless gateway (20) to automatically obtain a join key (208) for the wireless field device identifier. The join key is written (210) to the wireless field device (50) with the handheld field maintenance tool (100).
A method (60) of evaluating a potential location (30) to add a wireless field device (32) to an existing network of a plurality of existing wireless field devices (10) is provided. The method (60) includes placing (62) a handheld field maintenance tool (52) in the potential location (30) and causing the handheld field maintenance tool (52) to identify wireless field devices (10) within communicative range of the potential location (30). Information related to wireless communication at the potential location (30) is viewed. Methods (70, 250) are also provided for identifying a selected field device in a process installation using a handheld field maintenance tool (52).
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
H04W 24/00 - Supervisory, monitoring or testing arrangements
A handheld field maintenance tool (102) with improved functionality is provided. The handheld field maintenance tool (102) includes a keypad (122), a display (120), a short-range wireless transceiver (162) and a processor (152). The processor (152) is coupled to the keypad (122), the display (120) and the short-range wireless transceiver (162). The processor (152) is also coupled to memory (154) having a plurality of instructions stored therein, which instructions, when executed by the processor (152), cause the processor (152) to perform at least one of remote wireless display; remote wireless keypress injection; and wireless printing.
A wireless field maintenance adapter (114) includes a power source (132), a controller (130), a low-power radio- frequency communication module (122), and a wireless process communication protocol module (120). The controller (130) is coupled to the power source (132). The low-power radio- frequency communication module (122) is also coupled to the controller (130). The wireless process communication protocol module (120) is coupled to the controller (130). The controller (130) is configured to communicate through the wireless process communication protocol module (120) based on information received from the low-power radio-frequency communication module (122).
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
67.
MODEL PREDICTIVE CONTROLLER WITH TUNABLE INTEGRAL COMPONENT TO COMPENSATE FOR MODEL MISMATCH
An MPC controller technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC controller that performs better than traditional MPC techniques in the presence of process model mismatch. In particular, MPC controller performance is enhanced by adding a tunable integration block to the MPC controller that develops an integral component indicative of the prediction or other control error, and adds this component to the output of an MPC controller algorithm to provide for faster or better control in the presence of model mismatch, which is the ultimate reason for the prediction error in the first place. This technique enables the MPC controller to react more quickly and to provide better set point change and load disturbance performance in the presence of model mismatch, without decreasing the robustness of the MPC controller.
A process control management method in a computer system for configuring and supervising a process plant includes providing an interactive user interface to manage a plurality of objects in the process plant, where each of the plurality of objects corresponds to a physical or logical entity in the process plant, including generating a navigation pane to display a set of selectable items, each in the set of selectable items corresponding to a respective one of the plurality of objects, and generating a command pane to display a set of selectable controls, each in the set of selectable controls corresponding to a task to be performed on at least one of the plurality of objects in the process plant; receiving a selection of one of an item in the set of selectable items via the navigation panel and a control in the set of selectable controls via the command panel; determining an operational context based on the received selection, wherein the operational context corresponds to one of a range of actions applicable to the selection if the selection is an item selection, or a range of items to which the selection is applicable if the selection is a control selection; and adjusting one of the navigation pane or the command pane according to the operational context, including displaying a subset of selectable items in the navigational pane, wherein each in the subset of selectable items is within the range applicable to the selection, if the selection is a control selection, and displaying a subset of selectable controls in the command pane, wherein each in the subset of selectable controls is within the range applicable to the selection, if the selection is an item selection.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
69.
EFFICIENT DESIGN AND CONFIGURATION OF ELEMENTS IN A PROCESS CONTROL SYSTEM
A process control configuration method in a user interface of a computer system for developing control strategies of a process plant, where the user interface defines a screen area to display a plurality of independent panes therein, includes generating a first edit pane, including displaying a graphical representation of a first set of logical or physical entities for carrying out respective process control operations in the process plant; generating a second edit pane, including displaying a graphical representation of a second set of logical or physical entities for carrying out respective process control operations in the process plant, wherein each in the first set and the second set of logical or physical entities includes at least one input and at least one output, and wherein each of the first edit pane and the second edit pane defines an independent user interface screen within the screen area; receiving a first selection of an output of a first entity in the first set of logical or physical entities; receiving a second selection of an input to a second entity in the first second of logical or physical entities; and automatically generating respective visual indicators in each of the first edit pane and the second edit pane in response to receiving the first selection and the second selection, wherein the visual indicators depict respective endpoints of a connection between the first entity and the second entity.
G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
70.
WIRELESS MESH NETWORK WITH PINCH POINT AND LOW BATTERY ALERTS
A wireless mesh network includes a plurality of wireless devices and a gateway organized in a multi hop mesh topology. Each wireless device maintains and reports radio statistics to the gateway, and also reports battery conditions of its power source. The device manager communicates with the gateway and provides an alert indicating existence of a pinch point within the mesh network based upon the radio statistics. When a low battery condition is reported by a device, the device manager determines whether loss of that device is a pinch point or will cause a pinch point, and provides a low battery alert prioritized based upon the pinch point analysis.
A field device interface module (10) includes a connector (20), a plurality of terminals (50, 52), a protocol interface module (26), a controller (38) and a power supply module (30). The connector (20) is configured to operably couple to a computer (14). The terminals (50, 52) are operably coupleable to a field device (12). The protocol interface module (26) is coupled to the plurality of terminals (50, 52) and configured to generate signals in accordance with a process communication protocol. A power supply module (30) is coupled to the plurality of terminals (50, 52). The controller (38) is coupled to the protocol interface module (26) and to the power supply module (30) and is configured to measure a voltage across the plurality of terminals (50, 52) and selectively cause the power supply module (30) to provide power to the field device (12).
Wireless devices are provisioned to join a wireless mesh network by writing an individual or common join key and network identification information to the wireless device, and creating an association of the wireless device with a gateway of the network by providing the gateway with a unique device identifier for the wireless device. The writing of the join key to the wireless device is achieved without revealing the join key to a user.
A visualization tool for displays devices included within a self-organizing mesh network with respect to the physical space occupied by the network. The visualization tool receives an image representing the physical space occupied by the wireless mesh network, scale information defining the scale of the received image, and location information defining the location of each device within the physical space occupied by the network. Based on these inputs, the visualization tool displays the layout of the wireless mesh network with respect to the physical space occupied by the wireless mesh network.
An MPC adaptation and tuning technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC adaptation/tuning technique that performs better than traditional MPC techniques in the presence of process model mismatch. The MPC controller performance is enhanced by adding a controller adaptation/tuning unit to an MPC controller, which adaptation/tuning unit implements an optimization routine to determine the best or most optimal set of controller design and/or tuning parameters to use within the MPC controller during on-line process control in the presence of a specific amount of model mismatch or a range of model mismatch. The adaptation/tuning unit determines one or more MPC controller tuning and design parameters, including for example, an MPC form, penalty factors for either or both of an MPC controller and an observer and a controller model for use in the MPC controller, based on a previously determined process model and either a known or an expected process model mismatch or process model mismatch range. A closed loop adaptation cycle may be implemented by performing an autocorrelation analysis on the prediction error or the control error to determine when significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
75.
SIMPLIFIED ALGORITHM FOR ABNORMAL SITUATION PREVENTION IN LOAD FOLLOWING APPLICATIONS INCLUDING PLUGGED LINE DIAGNOSTICS IN A DYNAMIC PROCESS
Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device.
Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device.
A system for preventing abnormal situations in process plants is provided. A polynomial regression model (112) is employed to predict values of a monitored variable based on measured samples of a load variable. An abnormal situation is detected when a predicted value of the monitored variable differs from a measured value of the monitored variable by more than a predetermined amount. The system employs one or more algorithms for automatically determining an optimal order or degree of the polynomial regression model.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
78.
SYSTEM AND METHOD FOR RECOGNIZING AND COMPENSATING FOR INVALID REGRESSION MODEL APPLIED TO ABNORMAL SITUATION PREVENTION
A system for preventing abnormal situations in process plants is provided. A polynomial regression model is employed to predict values of a monitored variable based on measured samples of a load variable. An abnormal situation is detected when a predicted value of the monitored variable differs from a measured value of the monitored variable by more than a predetermined. The system recognizes when a data model is invalid and takes steps to compensate for the invalid model.
A process control system uses an asset data and search expert to collect data, or status information, pertaining to assets of a process plant from various sources or functional areas of the plant including. The collected information may then be accessed by a user through a user interface routine displaying a graphical user interface to that user's computer. The user may browse through status information on various assets, identifying them by device, unit, process, area, alert status, health, performance, or other data types. The asset data and search expert tracks user interaction with such plant data by, for example, tracking the types of search fields a user most frequently searches with or the type of information a user more frequently browses for. The expert automatically profiles this tracked information to develop user preferences that are later used in personalizing the reporting of asset data, personalizing searching for asset data, and personalizing the results of such searches. The expert may also automatically identify asset data that correlates with other asset data to present correlated asset data when the primary asset data is selected for viewing.
A system for preserving process variable data relating to the operation of a process is provided. The system is adapted to preserve process variable data obtained before, during, and after the occurrence of an abnormal situation or event. The preserved process variable data maybe communicated from smart field devices (106) or other intelligent equipment relating to the control (102) of the process to a process controller or other higher level control device. The process controller or other higher level control device may then cause the preserved data to be displayed for an operator or other maintenance personnel. The preserved data may also be provided to other process control modules or abnormal situation prevention systems for further analysis to develop methods for preventing the abnormal situation from recurring in the future, or for taking additional steps based on the abnormal situation data to minimize or prevent a further deterioration of the process operation.
A digital filter design algorithm is implemented directly within a process control field device or other process related equipment. Filter design parameters are exposed so that filter design parameter values may be provided to the digital filter design algorithm so that the digital filter design algorithm may calculate digital filter coefficients for a digital filter having desired frequency response characteristics. The digital filter design parameter values may be provided by a user, or may be provided as process variable data output from a process control field device or other process related equipment. Once the coefficients of the digital filter having the desired frequency response characteristics have been calculated, the digital filter may be applied to process variable data received by the process control field device or other process related equipment.
Improved functionality of handheld field maintenance tools (22) is provided. A user may interact with the handheld field maintenance tool (22) using a software application (118) that communicates with the tool (22) and with a manufacturer server (104). Tool information, including a unique tool (22) identifier, is uploaded from the tool and associated with at least some user information. The user is able to view additional and/or updated functionality information relative to one or more tools (22) with which the user is associated and obtain additional functionality electronically. The user also is provided with the ability to provide a tool name that is stored and displayed on the tool (22).
A wireless process communication adapter (116) is provided. The adapter (116) includes a plurality of plugs (120, 122) that are coupleable to a handheld field maintenance tool (102). A loop communication module (154) is operably coupled to the plurality of plugs (120, 122). The loop communication module (154) is configured to communicate digitally in accordance with a process loop communication standard protocol. A controller (150) is coupled to the loop communication module (154), and is configured to transform at least one message received from the loop communication module (154) to at least one corresponding wireless protocol packet. A wireless communication module (152) is coupled to the controller (150) and configured to receive the at least one corresponding wireless protocol packet and generate a wireless signal based upon the at least one wireless protocol packet.
Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection may include determination of the health and performance of one or more heat exchangers in the plant, and in particular, detection of a fouling condition of the one or more heat exchangers. Among the statistical measures, the detection may include calculation of an overall thermal resistance of the heat exchanger, which may be indicative under certain circumstances of heat exchanger performance and in particularly degradation of heat exchanger performance as a result of heat exchanger fouling.
A method and apparatus for verifying operation of process variable transmitters in process control or monitoring systems (100) is provided. A process variable is measured with a process variable transmitter (102) to verify operation of the process variable transmitter (108) by comparing the process variable with a reference. A data entry is placed in a database (130) which indicates operation of the process variable transmitter (102) has been verified.
A system for detecting abnormal operation of at least a portion of a process plant includes a composite model for modeling at least the portion of the process plant. The model may be configurable to include multiple regression models corresponding to multiple different operating regions of the portion of the process plant. A new model may be generated from two or more of the regression models, and the composite model may be revised to replace the two or more regression models with the new model. The system may also include a deviation detector configured to determine if the actual operation of the portion of the process plant deviates significantly from the operation predicted by the composite model. If there is a significant deviation, this may indicate an abnormal operation.
In a method for generating a model for modeling at least a portion of the process plant, M process variable data sets, where M is an integer, may be used to determine statistical data that may be utilized to scale process variable data sets. The M process variable data sets are scaled and then utilized to calculate intermediate model terms. For each additional process variable data set, it is scaled using the statistical data and then utilized to update the intermediate model terms. When an adequate number of process variable data sets have been processed, the model may be calculated using the intermediate model terms.
A Field Device Tool (FDT)-based application (200) is provided. The FDT-based application (200) includes at least one communication Device Type Manager (communication DTM) (208) and a router Device Type Manager (DTM) (204). The communication DTM corresponds with a type of communication protocol that an at least one plant asset (201) follows. The communication DTM (208) is configured to provide an interface for communication between the FDT-based application (200) and the communication protocol that the plant asset (201) follows. The router DTM (220) is coupleable to an asset optimization device manager (212) that includes electronic device description language (EDDL), the router DTM (220) is configured to transfer data from the asset optimization device manager (212) to the at least one communication DTM (208) for communication with the plant asset (201).
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
A calibrator (100, 200, 300, 350, 500) for field devices is provided. In one aspect, the calibrator (100, 200) has the ability to communicate in accordance with at least two process communication protocols, and tests an attached process connection before engaging communication. In another aspect, the calibrator (500) includes isolation circuitry to facilitate compliance with at least one intrinsic safety requirement, while communicating with field devices using an all -digital process communication protocol. In another aspect, a method (600) of calibrating field devices is provided which accesses device descriptions of the field devices to generate calibration tasks.
In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a plurality of process variables may be analyzed to determine whether any of a plurality of faults associated with the process plant exist. If one or more faults are detected, one or more indicators may be generated. Analyzing the values of the plurality of process variables may include utilizing a coefficient matrix. The coefficient matrix may be generated based on process variable data corresponding to the known occurrences of faults.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
A method and system for detecting and/or predicting abnormal levels of catalyst loss in a fluid catalytic cracking unit. The method and system measures a differential pressure across portions of a fluid catalytic cracker, such as a reactor cyclone or a regenerator cyclone, and determines abnormal catalyst loss when the differential pressure changes significantly from a baseline differential pressure. The claimed method and system implements algorithms using computing devices to detect or predict an abnormal condition based on the change in a monitored differential pressure in a fluid catalytic cracking unit.
C10G 11/18 - Catalytic cracking, in the absence of hydrogen, of hydrocarbon oils with preheated moving solid catalysts according to the "fluidised bed" technique
93.
MULTIVARIATE MONITORING AND DIAGNOSTICS OF PROCESS VARIABLE DATA
A system and method of monitoring and diagnosing on-line multivariate process variable data in a process plant, where the multivariate process data comprises a plurality of process variables each having a plurality of observations, includes collecting on-line process data from a process control system within the process plant when the process is on-line, where the collected on-line process data comprises a plurality of observations of a plurality of process variables, performing a multivariate statistical analysis to represent the operation of the process based on a set of collected on-line process data comprising a measure of the operation of the process when the process is on-line, where the representation of the operation of the process is adapted to be executed to generate a result, storing the representation of the operation of the process and the set of collected on-line process data, and generating an output based on a parameter of the representation of the operation of the process, where the parameter of the representation of the operation of the process comprises one or more of a result generated by the representation of the operation of the process, a process variable used to generate the representation of the operation of the process and the set of collected on-line process data.
A method and system for monitoring a process in a process plant includes collecting data from a process control system within the process plant, where the data is representative of a normal operation of the process when the process is on-line and operating normally, performing a multivariate statistical analysis to represent the normal operation of the process based on a set of collected on-line process data comprising a measure of the normal operation of the process when the process is on-line and operating normally, and representing a real¬ time on-line operation of the process using monitored on-line process data comprising a measure of a real-time operation of the process when the process is on-line as an input to the representation of the normal operation of the process.
Methods and systems to detect transient operations from abnormal operations, and to detect abnormal operations in a coker heater, include collecting on-line process data. The collected on-line process data is generated from a plurality of process variables of the process, or coker heater. A first representation of the operation of the process, or coker heater, is generated based on a first set of the collected on-line process data generated from a first set of the process variables. The first representation is adapted to be executed to generate a first result. A second representation of the operation of the process, or coker heater, is generated based on the first result and based on a second set of the collected on-line process data generated from a second set of the process variables. The second representation is adapted to be executed to generate a prediction of data generated from the second set of the process variables. The prediction is analyzed to detect an abnormal operation or to detect whether one or more abnormal operations comprises a transient operation of the process.
Methods and systems to detect steady-state operations in a process of a process plant include collecting process data. The collected process data is generated from a plurality of process variables of the process. A multivariate statistical model of the operation of the process is generated using the process data. The multivariate statistical model may be generated from a principal component analysis. The model is executed to generate outputs corresponding to the most significant variations in the process. Statistical measures of the outputs are generated and used to determine whether a steady-state or unsteady- state is related to the process.
Methods and systems to detect abnormal operations in a process of a process plant include collecting on-line process data. The collected on-line process data is generated from a plurality of dependent and independent process variables of the process, such as a coker heater. A plurality of multivariate statistical models of the operation of the process are generated using corresponding sets of the process data. Each model is a measure of the operation of the process when the process is on-line at different times, and al least one model is a measure of the operation of the process when the process is on-line and operating normally. The models are executed to generate outputs corresponding to loading value metrics of a corresponding dependent process variable, and the loading value metrics are utilized to detect abnormal operations of the process.
A system and method of monitoring and diagnosing on-line multivariate process variable data in a process plant, where the multivariate process data comprises a plurality of process variables each having a plurality of observations, includes collecting on-line process data from a process control system within the process plant when the process is on-line, where the collected on-line process data comprises a plurality of observations of a plurality of process variables and where the plurality of observations of the set of collected process data comprises a first data space having a plurality of dimensions, performing a multivariate statistical analysis to represent the operation of the process based on a set of collected on-line process data comprising a measure of the operation of the process when the process is on-line within a second data space having fewer dimensions than the first data space, perfo꧀ning a univariate analysis to represent the operation of the process as a multivariate projection of the on-line process data by a univariate variable for each of the process variables, where the univariate variable unifies the process variables, and generating a visualization comprising a first plot of a result generated by the multivariate statistical representation of the operation of the process and a second plot of a result generated by the univariate representation of the operation of the process.
A method and system of monitoring multivariate process data in a process plant, where the multivariate process data comprises a plurality of process variables each having a plurality of observations, includes defining each process variable as a process variable vector comprising a set of observation components, where the set of observation components comprises time dependent process data corresponding to the observations of the process variable, calculating a multivariable transformation as a function of a plurality of process variable vector transformations each corresponding to one of the process variables, where each process variable vector transformation is a function of a univariate variable unifying the process variables, and representing the operation of the process based on the multivariable transformation, where the representation of the operation of the process designates a multivariate projection of the process data by the univariate variable for each of the process variables.
A method and system for detecting and/or predicting abnormal solids buildup in a main fractionator bottom of a fluid catalytic cracking system measures one or more process parameters of the fluid catalytic cracking system (such as a differential pressure across a reactor cyclone, a noise after the main fractionator bottom, a heat transfer at the steam generator, and'or a differential pressure across the main fractionator) and determines abnormal solids buildup when the measured process parameter(s) changes significantly from a baseline value, fhe method and system implements algorithms using computing devices to detect or predict an abnormal condition based on the change in the process parameter.
C10G 11/18 - Catalytic cracking, in the absence of hydrogen, of hydrocarbon oils with preheated moving solid catalysts according to the "fluidised bed" technique
G05B 1/00 - Comparing elements, i.e. elements for effecting comparison directly or indirectly between a desired value and existing or anticipated values