Dynamic industrial artificial intelligence configuration and tuning
Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.
1 . A system comprising a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a configuration component configured to construct a graphical representation of an industrial process, wherein
the graphical representation comprises a group of models,
a first model of the group of models represents operation of a device of the industrial process according to a first configuration of the device, and
the first model generates an output based on a value of output data received from the device;
a visualization component configured to present the graphical representation of the process on a human-machine interface (HMI); and
an artificial intelligence (AI) component configured to, in response to determining that a value of the output does not satisfy a constraint applied to the output:
determine a second model, from among multiple models stored in a model repository, capable of representing the device and producing an output that satisfies the constraint,
replace the first model with the second model in the graphical representation, and
send configuration data defined by the second model to the device, wherein sending the configuration data to the device causes the device to operate in accordance with a second configuration associated with the second model.
2 . The system of claim 1 , wherein
the output of the first model comprises a value of a parameter generated by at least one function included in the first model.
3 . The system of claim 1 , wherein the AI component is further configured to:
in response to determining that the value of the output does not satisfy the constraint:
generate a notification that operation of the device represented by the first model does not satisfy the constraint.
4 . The system of claim 3 , wherein the visualization component is further configured to present the notification on the HMI.
5 . The system of claim 3 , wherein the AI component is further configured to:
send the configuration data to the device with an instruction to apply the configuration data to the device to control operation of the device.
6 . The system of claim 5 , wherein the configuration component is further configured to:
instruct the device to transmit a current operating configuration of the device;
receive the current operating configuration from the device;
perform a comparison of the current operating configuration of the device with the second configuration; and
confirm that the current operating configuration of the device matches the second configuration based on a result of the comparison.
7 . The system of claim 3 , wherein the device is one of a sensor, an actuator, a valve, an industrial controller, a motor drive, a telemetry device, a meter, a smart device, a device configured to monitor operation of a component/equipment included in the industrial process, or a device configured to control operation of a component/equipment included in the industrial process.
8 . The system of claim 1 , wherein the AI component is further configured to record an operation conducted on the HMI, wherein
the operation is an interaction with the graphical representation, and
the interaction with the graphical representation comprises at least one of adding a model to the group of models, removing a model from the group of models, associating a device configuration with a model of the group of models, adding a device, removing a device, implementing a configuration at a device included in the industrial process, responding to a notification, selecting data to store, selecting information to store, selecting a layout representation of the graphical representation, selecting a model representation of the graphical process, selecting a node associated with a model of the group of models, adjusting a constraint to a model of the group of models, connecting a first model to a second model, selecting a model type, or constructing a model.
9 . The system of claim 8 , wherein the AI component is further configured to replicate the interaction with the graphical representation based on an interpretation of the operation.
10 . The system of claim 8 , wherein the graphical representation of the industrial process comprises at least one of:
a real-time version presenting a real-time graphical representation of the industrial process; or
an offline version presenting a graphical representation of the industrial process based on at least one of real-time data received from one or more devices operating in the industrial process or historical data captured from a prior operation of the one or more devices operating in the industrial process.
11 . The system of claim 10 , wherein user interaction with the graphical representation is via the offline version.
12 . The system of claim 11 , wherein the configuration component is further configured to review an authorization to determine whether authorization exists prior to implementing, to the real-time version, a model or configuration represented on the offline version.
13 . The system of claim 1 , wherein
at least one of the first model or the second model comprises a parametric model, a parametric hybrid model, a linear model, a non-linear model, a kinetic model, a first principles reasoning model, a solver, a historical data model, a cost function analysis model, a regression cost function model, a binary classification cost function model, a multi-class classification cost function model, a mixed-integer non-liner program model, a deep learning-based model, a backpropagation model, a static backpropagation model, a recurrent backpropagation model, a gradient computation model, a chain rule model, an error determination model, or a mathematical model configured to represent operation of a component in the process, and
the component is a device, a group of devices, or a component block.
14 . The system of claim 1 , wherein
the output generated by the first model is provided to a third model of the group of models as an input to the third model, and
the constraint applied to the output is defined by the third model.
15 . A computer-implemented method for visualizing an industrial process, comprising:
constructing a graphical representation of an industrial process, wherein
the graphical representation includes a first model representing operation of a device of the industrial process according to a first configuration of the device, and
the first model generates an output based on a value of output data generated by the device;
monitoring the value of the output data received from the device represented by the first model; and
in response to determining that the value of the output data does not satisfy a constraint applied to the output:
identifying a second model, from among multiple models stored in a model repository, capable of representing the device and producing an output that satisfies the constraint;
replacing the first model with the second model in the graphical representation; and
sending configuration data defined by the second model to the device, wherein the sending of the configuration data to the device configures the device to operate in accordance with a second configuration associated with the second model.
16 . The computer-implemented method of claim 15 , further comprising presenting, on a human-machine interface (HMI), the graphical representation.
17 . The computer-implemented method of claim 15 , wherein
at least one of the first model or the second model is one of a parametric model, a parametric hybrid model, a linear model, a non-linear model, a kinetic model, a first principles reasoning model, a solver, a historical data model, a cost function analysis model, a regression cost function model, a binary classification cost function model, a multi-class classification cost function model, a mixed-integer non-liner program model, a deep learning-based model, a backpropagation model, a static backpropagation model, a recurrent backpropagation model, a gradient computation model, a chain rule model, an error determination model, or a mathematical model configured to represent operation of a component in the industrial process, and
the component is a device, a group of devices, or a component block.
18 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a process to cause the processor to:
construct a graphical representation of an industrial process, wherein the graphical representation includes a first model representing operation of a device of the industrial process according to a first configuration of the device;
monitor the value of the output data received from the device represented by the first model; and
in response to determining that the value of the output data does not satisfy a constraint applied to the output:
identify a second model, from among multiple models stored in a model repository, capable of representing the device and producing an output that satisfies the constraint;
replace the first model with the second model in the graphical representation; and
send configuration data defined by the second model to the device, wherein sending the configuration data to the device configures the device to operate in accordance with a second configuration associated with the second model.
19 . The computer program product of claim 18 , wherein the program instructions executable by the processor further cause the processor to:
in response to the determining that the value of the output data does not satisfy a constraint applied to the output:
generate a notification that operation of the device represented by the first model does not satisfy the constraint; and
present, on a human machine interface, the graphical representation and the notification.
20 . The computer program product of claim 19 , wherein the program instructions executable by the processor further cause the processor to, in response to determining that the value of the output data does not satisfy the constraint:
transmit an instruction for the device to apply the configuration data to the device to control operation of the device.