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 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 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 system and method for monitoring and controlling production of batches of products in an industrial process, the method comprising: receiving, by a processing circuit, data describing a batch of products generated in an industrial process from one or more data sources; contextualizing, by the processing circuit, the data describing the batch of products generated in the industrial process; generating, by the processing circuit, a batch data model based on the contextualized data; executing, by the processing circuit, the batch data model to determine key performance indicators for the batch of products; comparing, by the processing circuit, the key performance indicators to pre-determined key performance indicators; and performing an automated action based on a result of the comparison.

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 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 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 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.