Abstract: A method may include receiving, via graphical user interface (GUI) of a processing system, a selection of a dataset associated with one or more operations of one or more industrial automation components of an industrial system. The method may also include receiving, via the GUI of the processing system, a set of input variables associated with the dataset, receiving a target variable associated with the dataset, and receiving a model type for analyzing the dataset. The method may also involve determining, via the processing system, a contribution of each of the set of input variables to the target variable based on the model type; and generating, via the processing system, a visualization representative of one or more statistical relationships between each of the set of input variables and the target variable based on the contribution of each of the set of input variables to the target variable.

Abstract: A method may include receiving, via a processing system, a selection of a first dataset associated with one or more operations of one or more industrial automation components of an industrial system that may perform a batch operation. The method may involve generating an optimized dataset based on the dataset, receiving a second dataset associated with one or more additional operations of one or more additional industrial automation components of an additional industrial system that may perform an additional batch operation, and determining one or more deviations between the optimized dataset and the second dataset. The method may also involve determining a contribution of each of a set of parameters to the one or more deviations and generating a visualization representative of the contribution of each of a set of parameters to the deviation.

Abstract: An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.

Abstract: A system for distributing wear on multiple movers in an independent cart system includes a machine learning model executing on a processor. The machine learning model may include models of operation for each of the movers, and the machine learning model is operative to receive multiple inputs for each of the movers. Each of the inputs corresponds to an operating condition for one of the movers as the mover travels along a track for the independent cart system. Each of the inputs are received for each of the movers over multiple runs along the track, and the inputs received generate a training set of data for the movers. A weighting value is determined for each of the movers as a function of the training set of data, where the weighting value corresponds to a level of wear present on each of the movers.

Abstract: A method may include receiving, via a processing system, a request for information associated with an industrial automation system from a user, identifying a prompt associated with the request, and identifying one or more datasets associated with the request based on the prompt and the information. The method may also involve receiving the one or more datasets from one or more data sources, formatting the request and the one or more datasets into a package, and sending the package to a generative artificial intelligence (AI) system. The method may then involve receiving a response from the generative AI system, such that the response may be presented via a display of a human machine interface (HMI) system.

Abstract: An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.

Abstract: An industrial alarm monitoring system leverages generative artificial intelligence (AI) to perform dynamic monitoring and analysis of a customer's industrial processes, identify potential or active performance issues or alarm conditions, and assist users in resolving these issues. The system monitors and collects operational and status data from industrial devices and assets of industrial automation systems and stores information regarding active and historical alarm conditions indicated by this data in an alarm repository. Users can submit natural language requests for assistance with, or information about, active or historical alarms to the system, which leverages trained custom models and a generative AI model to process these requests. The system can formulate natural language alarm resolution guidance based on analysis of the user's request, content of the custom models, responses prompted from the generative AI model, and relevant information about the alarm condition obtained from the alarm repository.

Abstract: Identity-federated smart contracts can be combined with an identity provider to provide uniform, customizable, secure, and immutable authentication across many different domains. Such can address the challenges of cross-domain authentication in the context of a decentralized environment such as a decentralized IIoT environment in which a host application resident in a host domain can functionally integrate with, and potentially be bundled or packaged as a single product, many remote applications that reside in many different (e.g., customer) domains that use different forms of authentication, and where some domains may have no authentication mechanisms at all.

Abstract: A digital technology transfer system transforms technology transfer documents to a set of digitized manufacturing procedures and operations documentation. The system can transform a technology transfer document to a hierarchical structured model representing a package, or product to be manufactured, and the process for manufacturing the product. The resulting package model can be integrated into a larger model representing an ecosystem of manufacturing entities and plant facilities by assigning steps of the manufacturing process to one or more selected production lines. The system allows participants in the ecosystem to browse the hierarchical model to view information about the manufacturing entities, their plant facilities, and the packages assigned to the respective facilities. The system offers filtered role-specific views of the technology transfer documents, their approval statuses, and their plant assignments.

Abstract: A non-transitory computer readable medium stores instructions that, when executed by a processor, cause the processor to receive, from one or more processing nodes, data representative of usage and inventory of one or more software assets by one or more industrial automation devices of an industrial automation system that are communicatively coupled to the one or more processing nodes, determining a data delta for the industrial automation system that represents differences between the software asset data and a previous iteration of the software asset data, encrypt the data delta for the industrial automation system, and transmit the encrypted data delta for the industrial automation system to a remote server.

Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving data associated with one or more industrial devices of an industrial system, and retrieving one or more pre-processing files and one or more training datasets files associated with a model from a database, wherein the one or more pre-processing files are configured to transform the data, and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time. The instructions cause the processing circuitry to perform operations including receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display, and generating the model based on the training dataset files and the one or more inputs.

Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving a set of data associated with industrial devices of an industrial system, and retrieving pre-processing files and training datasets files associated with the industrial devices from a database, wherein the pre-processing files are configured to transform the data for generating a model representative of the industrial devices, and wherein the training dataset files are representative of operational characteristics of the industrial devices over time.

Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving data associated with industrial equipment, pre-processing the data using pre-processing files associated with a modeling technique to generate pre-processed data, generating training dataset files based on the pre-processed data, and generating a model representative of expected operations of the industrial equipment based on the training dataset files.

Abstract: A method comprises determining that a batch generated in an industrial process (IP) is anomalous at a sample point k during the batch, the batch is ongoing; determining a process variable (PV) of the IP based on a variable contribution of the PV towards the batch being anomalous at the sample point k; determining a recommended value of the PV based on an anomaly metric corresponding to the sample point k of an assessment batch, the assessment batch is created based on sample(s) of the batch at the sample point k and the recommended value of the PV, the anomaly metric corresponding to the sample point k of the assessment batch is determined based on a T2-statistic metric corresponding to the sample point k and a Q-statistic metric corresponding to the sample point k of the assessment batch; and adjusting the IP based on the recommended value of the PV.

Abstract: An illustrative method includes a batch analytic system receiving batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process, applying, for each process variable among the J process variables of the industrial process, a first function to K values of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch, aggregating first feature values corresponding to the J process variables that are determined for the batch using the first function to form a batch representation of the batch, and performing an operation using the batch representation of the batch.

Abstract: An illustrative method includes a batch analytic system receiving batch data of a batch generated in an industrial process, wherein the batch data includes a set of samples associated with the batch, the batch is complete and has a first batch length, determining a reference batch based on a plurality of non-anomalous batches generated in the industrial process, wherein each non-anomalous batch has a same second batch length, generating a batch representation of the batch based on the batch data of the batch and the reference batch, wherein the batch representation of the batch aligns with the reference batch and has the second batch length associated with the reference batch, and performing an operation using the batch representation of the batch.

Abstract: An illustrative method includes an anomaly detection system determining, for a batch generated in an industrial process, a T2-statistic metric and a Q-statistic metric of the batch in a principal component analysis (PCA) model associated with the industrial process, determining an anomaly metric of the batch based on the T2-statistic metric and the Q-statistic metric of the batch in the PCA model, determining that the batch is anomalous based on the anomaly metric of the batch, and performing an operation in response to determining that the batch is anomalous.

Abstract: Systems and methods for detecting anomalies in network communication in an industrial automation system. An anomaly detection system, a decentralized system, may identify IoT devices within the network communication and corresponding communication metrics. Using the communication metrics between the identified IoT devices, the anomaly detection system may generate a social network model that is indicative of expected network communication properties. By analyzing social network metrics and overall entropy of the network communication in real time, the anomaly detection system may identify anomalies that may be associated with potential network vulnerabilities.

Abstract: A non-transitory computer readable medium stores instructions that, when executed by a processor, cause the processor to receive telemetry data representing usage of one or more software assets by one or more industrial automation devices within an industrial automation system operated by an enterprise, reference a local blockchain that is a private extension of a global decentralized blockchain to determine one or more licenses associated with the one or more software assets, determine one or more usage parameters for the one or more software assets based on the telemetry data and the one or more licenses, update the local blockchain and the global decentralized blockchain based on the and the usage parameters, and enable usage of the one or more software assets by the one or more industrial automation devices based on the one or more usage parameters and one or more thresholds defined by the one or more licenses.

Abstract: A digital technology transfer system transforms technology transfer documents to a set of digitized manufacturing procedures and operations documentation. The system can transform a technology transfer document to a hierarchical structured model representing a package, or product to be manufactured, and the process for manufacturing the product. The resulting package model can be integrated into a larger model representing an ecosystem of manufacturing entities and plant facilities by assigning steps of the manufacturing process to one or more selected production lines. To reduce dependency on custom-built parsers for each type of document format, the system integrating both custom and general parsing mechanisms into a scalable parser orchestration engine.