Abstract: Systems, methods, and apparatuses of mover (or other track-based or autonomous mover component) parameter determination are provided. The system can determine a scaling factor based on a first plurality of input parameters. The system can generate an offset based on a second plurality of input parameters. The system can determine, based on the scaling factor and the offset input into a machine learning model trained on a plurality of input parameters that include at least one of the first plurality of input parameters and at least one of the second plurality of input parameters, a state of health. The system can map the state of health to a mover of a plurality of movers configured to travel along a track. The system can output an indication of the state of health mapped to the mover to display by a computing device.

Abstract: A digital technology transfer system transforms technology transfer documents to a set of digitized manufacturing procedures and operations documentation. The system can leverage generative artificial intelligence (AI) and associated trained custom models to 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 leverages generative artificial intelligence (AI) and associated trained models to extract and organize document content, and to map the document content to appropriate target data structures that can be consumed by control systems and devices.

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.

Abstract: A digital technology transfer system transforms technology transfer documents to a set of digitized manufacturing procedures and operations documentation. The system can leverage generative artificial intelligence (AI) and associated trained custom models to 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 leverages generative artificial intelligence (AI) and associated trained models to extract and organize document content, and to map the document content to appropriate target data structures that can be consumed by control systems and devices.

Abstract: Disclosed herein are methods and systems that provide hyperparameter tuning for generative artificial intelligence prompt engineering to users prompting a generative artificial intelligence (GAI) model to return an executable logic code output. Where a user inputs a portion, a prompt is received and an iteration of a hyperparameter tuning process is executed. The assistant receives the prompt from the user and generates a number of hyperparameter sets. Each of the hyperparameter sets and a copy of the prompt are used to generate a number of complete prompts, where each complete prompt corresponds to a hyperparameter set. The complete prompts are submitted to a generative artificial intelligence model, which returns a number of responses corresponding to each complete prompt. A user selects a response, and the hyperparameters associated with the choice are the basis of a next hyperparameter tuning process iteration.

Abstract: An industrial technical support system leverages generative artificial intelligence (AI) techniques to generate technical support guidance and recommendations in response to natural language technical support requests submitted as natural language input. The system can maintain and leverage one or more sets of custom models trained with sets of domain-specific training data specific for different industrial domains. When a natural language technical support query is received, the system leverages the domain-specific training data as well as responses prompted form a generative AI model to formulate and render technical support recommendations for addressing a performance or design issue described by the query.

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.

Abstract: A digital technology transfer system transforms technology transfer documents to a set of digitized manufacturing procedures and operations documentation. The system can leverage generative artificial intelligence (AI) and associated trained custom models to 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 leverages generative artificial intelligence (AI) and associated trained models to extract and organize document content, and to map the document content to appropriate target data structures that can be consumed by control systems and devices.

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: 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: 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 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: 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: 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 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 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 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.