Rockwell Automation Technologies Incorporated

Wei Dai

Bijan Sayyarrodsari

Kadir Liano

Alexander Barton Smith

Yash P Puranik

Alexander N Berman

Richard W Arling

Bijan Sayyarodsari

A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Systems and methods for variable processing of streamed sensor data

For updating a computer vision model, a method converts a user input drawing including a user annotation of a first image in a drawing format to a training format image in a training format for a computer vision model. The method generates a training-representation drawing from the training format image. The training-representation drawing includes an image inference for the first image. The method receives user feedback for the training-representation drawing in the drawing format. The method updates the computer vision model based on the user feedback. The method generates an image inference for a second image based on the updated computer vision model and generates model-health metrics, agreement-metrics, and a sortable image index to explain image inferences with respect to guided user annotation of the second image. The method caches partial results from the image-inferences to afford quicker updating of computer vision models, affording more iterative model-development than ad-hoc model-evaluation.

Computer vision model drawing interface

A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

Systems and methods for retraining a model a target variable in a tiered framework

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving image data after an operation is performed by an industrial automation device on a product; analyzing the image data based an object-based image analysis (OBIA) model to classify the product as one of a plurality of conditions related to manufacturing quality and the OBIA model includes property layers associated with features related to a manufacturing of the product; determining whether the one of the conditions indicates an anomaly being present in the product; sending a notification indicative of the one of the plurality of conditions is presently associated with the product; identifying a property layer associated with classifying the one of the plurality of conditions; and updating the OBIA model based on the property layer and the input indicative of the anomaly being incorrectly associated with the product.

Automated monitoring using image analysis

A computer-readable medium may include instructions that may cause a processor to perform operations that may include receiving audio data representative of sound waves generated by industrial devices and extracting features from the audio data. The features may be representative of a portion of the audio data. The operations may also include identifying a subset of the features based on distances between each of the plurality of features in an information space. The information space may include known clusters. The operations may then include determining that the subset of the features corresponds to an unknown cluster in the information space, performing a constrained classification operation based on each feature of the subset of the features to identify a new known cluster for the information space, and modifying operations of the industrial devices based on the new known cluster.

Automated diagnosis of augmented acoustic measurement in industrial environments

A method for operating an industrial automation system may include receiving, via a first module of a plurality of modules in a control system, a plurality of datasets via at least a portion of the plurality of modules. The plurality datasets may include raw values without context regarding the plurality datasets. The method may then include identifying a subset of the plurality of datasets that influences a value of a target variable by analyzing the data without regard to the context, modeling a behavior of the target variable over time based on the subset of the plurality of datasets, and adjusting one or more operations of an automation device based on the model.

Systems and methods for locally modeling a target variable

A modular analysis engine provided classification of variables and data in an industrial automation environment. The module may be instantiated upon receipt of an input data structure, such as containing annotated data for any desired variables related to the machine or process monitored and/or controlled. The data may be provided in a batch or the engine may operate on streaming data. The output of the module may be a data structure that can be used by other modules, such as for modeling, optimization, and control. The classification may allow for insightful analysis, such as for textual classification of alarms provided in the automation setting.

Classification modeling for monitoring, diagnostics optimization and control

An industrial automation system may include an automation device and a control system communicatively coupled to the automation device. The control system may include a first module of a number of modules, such that the first module may receive an indication of a target variable associated with the industrial automation device. The first module may then receive parameters associated with the target variable, identify a portion of data points associated with controlling the target variable with respect to the parameters, generate a model of each data point of the portion over time with respect to the parameters based on the data points, determine functions associated with the model. The functions represent one or more relationships between the each data point of the portion with respect to controlling the target variable. The first module may then adjust one or more operations of the automation device based on the functions.

Systems and methods for controlling industrial devices based on modeled target variables

An industrial automation system may include an automation device and a control system. The control system may be communicatively coupled to the automation device and may include a first module that may receive a request for information regarding the automation device. The first module may then determine information related to the request for information based on one or more datasets locally available to the first module, alter the information based on a signal configured to obscure the information, and send encrypted information to a second module of the plurality of modules.

Systems and methods for encrypting data between modules of a control system

One embodiment of the present disclosure describes an industrial system, which includes a control system that controls operation of an industrial process by instructing an automation component in the industrial system to implement a manipulated variable setpoint. The control system includes a process model that model operation of the industrial process, control optimization that determines the manipulated variable setpoint based at least in part on the process model, a control objective function, and constraints on the industrial process, in which the control objective function includes a tuning parameter that describes weighting between aspects of the industrial process affected by the manipulated variable setpoint; and tuning optimization circuitry that determines the tuning parameter based at least in part on a tuning objective function, in which the tuning objective function is determined based at least in part on a closed form solution to an augmented version of the control objective function, which includes the constraints as soft constraints.

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Austin, TX, United States of America

Wei Dai