Rockwell Automation Technologies Incorporated

Caterpillar Inc.

Alexander Barton Smith

Kadir Liano

Bijan Sayyarrodsari

Wei Dai

Yash P Puranik

Apurva Prakash Samudra

Jonathan D Walter

Bijan Sayyar-Rodsari

Jonathan M Wise

Eugene Mourzine

Olivia M Leslie

Samuel L Massari

Perry Dwain Converse

David M Miller

Mike Snopko

Bijan Sayyarrodsar

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 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

An analytics engine is provided for industrial automation applications. The engine may be modular, and may be instantiated upon receipt of a data structure, such as containing annotated data from or relating to a monitored and/or controlled machine or process. The module may be data-driven so that it is instantiated only as needed, upon receipt of the input data structure. The module then carries out analysis on the data, and outputs a data structure that can be used for further analysis, or directly by other modules for modeling, classification, optimization and/or control.

Base analytics engine modeling for monitoring, diagnostics optimization and control

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

A modular modeling engine is provided for industrial automation applications. The module may be instantiated upon demand, such as upon receipt of annotated data for a system or process being monitored and/or controlled. The model is agnostic insomuch as little or no prior knowledge is required of the system or process. Variables, functions, and their combinations are selected and the model is refined automatically. A data structure is received for instantiation of the model, and following modeling, a similar data structure is produced. The module may be used together with other modules for caning out complex automation processing at the same or multiple levels in an automation setting.

Automatic modeling for monitoring, diagnostics, optimization and control

The embodiments described herein include one embodiment that provides a control method that includes connecting a first controller to a control system; receiving control system configuration data from a database, in which the configuration data comprises holistic state data of a second controller in the control system; and configuring operation of the first controller based at least in part on the configuration data received.

Control system database systems and methods

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

System and method for improving operation of an industrial automation system, which includes a control system that controls operation of an industrial automation process. The control system includes a feature extraction block that determines extracted features by transforming process data determined during operation of an industrial automation process based at least in part on feature extraction parameters; a feature selection block that determines selected features by selecting a subset of the extracted features based at least in part on feature selection parameters, in which the selected features are expected to be representative of the operation of the industrial automation process; and a clustering block that determines a first expected operational state of the industrial automation system by mapping the selected features into a feature space based at least in part on feature selection parameters.

Predictive monitoring and diagnostics systems and methods

An industrial controller within an industrial automation environment is provided. The industrial controller includes a programming logic controller, configured to control an industrial device, and a predictive module, coupled with the programming logic controller. The predictive module is configured to receive configuration data from the programming logic controller, create a user-defined controller tag, and transfer the user-defined controller tag to the programming logic controller. The predictive module is also configured to receive operational data from the programming logic controller, and calculate operation monitoring data and value estimation data based on the operational data. The predictive module is further configured to write the operation monitoring data and value estimation data to the user-defined controller tag.

Industrial control system with predictive machine learning module

Configuring and operating control systems using a database

A system and method for synchronizing a frequency of plurality of variable frequency generators with a variable frequency load over a variable frequency bus independent of a frequency conversion stage. A synchronization controller is configured to determine an optimal bus frequency of the variable frequency bus based on at least one power demand requirement of the variable frequency load operatively connected to the variable frequency bus. With the optimal frequency, an available power range supplied by the plurality of variable frequency gensets at the optimal bus frequency can be determined. The synchronization controller then asymmetrically loads the variable frequency load to the plurality variable frequency gensets at the optimal bus frequency based on the operating range of each variable frequency genset and recursively updates the optimal bus frequency based on operational statistics of the asymmetrically loaded variable frequency gensets.

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Round Rock, TX, United States of America

Alexander Barton Smith