Abstract: A system may include a data delivery pipeline communicatively coupled to one or more microservices that receive a dataset transmitted through the data delivery pipeline. The system may also include a first microservice that receives a first dataset corresponding to operation technology (OT) data or information technology (IT) data and determines a second dataset based on the first dataset. The system may also include a second microservice that receives the second dataset from the first microservice via the data delivery pipeline, determines an action to perform in an industrial automation component of an industrial automation system based on an analysis of the second dataset, and transmits the action to the industrial automation component via the data delivery pipeline.

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 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 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 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 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 cloud-based operator training system includes a snapshot management architecture, which provides a hybrid system for generation of control system level scenarios and system-state snapshots, and which can improve the fidelity of a training simulation. By implementing the simulation system on a cloud platform, the system can generate a large and growing set of snapshot files representing various control states and corresponding process states. These files can then be leverage during operator training sessions to yield high fidelity simulated system operation.

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 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 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: Various embodiments relate to systems and methods for providing machine learning of supervised and unsupervised data by: receiving a set of industrial data associated with one or more industrial components within an industrial system; generating a classification for each of the set of industrial data using each of a set of models; generating an evaluation value for each of the set of models based on the classifications for each industrial data; and selecting one or more models according to the evaluation values.

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: Data reduction services are implemented on one or more nodes of an IIoT data pipeline to intelligently determine an appropriate data reduction strategy based on characteristics of the incoming data. In one or more embodiments, data reduction components on the pipeline node or on an edge device define different data filtering rules or algorithms that are selectively applied to streaming time-series data based on a probability distribution of the data. The data pipeline node performs real-time distribution analysis on the streaming data to determine whether the data has a unimodal distribution, a multimodal distribution, or no mode, and selects one of the data filtering rules based on this determined probability distribution. In this way, the data is intelligently reduced in a manner that retains critical information within the reduced data set while achieving a high level of data reduction.

Abstract: A monitor provides run time monitoring of the input values to a signature analyzer having a machine-learning engine to compare statistics measuring the domain of those input values to the domain of input values used in the training set of the machine-learning engine. This comparison yields an indication of whether retraining of the machine-learning engine is required without analysis of the output of the machine-learning engine algorithm or the need for errors to become manifest.

Abstract: An industrial data pipeline architecture described supports annotation of raw data streams into scalable and durable cloud-level storage. A dynamic data mapping process collects industrial data from data tags that have been annotated with metadata indicating that the tags are to be made available for visualization, and connects data from these tags to a data aggregator object of a node-level or edge-level visualization tool. The data aggregator object dynamically adjusts its data collection footprint to include the annotated data tags, which are dynamically associated with the physical layout of the industrial equipment in the visualization. Visualization objects for a specified plant configuration act as data context containers that can be added to a plant visualization panel, and allows the annotated tags to be selected to build visualizations of selected data items.

Abstract: A reactive buffering system for use in IIoT data pipelines dynamically adjusts data accumulation and delivery by a node of a pipeline based on aggregated downstream metrics representing current data processing latencies of downstream nodes. Based on these downstream performance metrics, a reactive node that adjusts the size of the next data batch to be sent to an adjacent downstream node. The nodes of the data pipeline are configured to support a request-response based handshaking protocol whereby the nodes that send data to downstream nodes maintain up-to-date performance level information from adjacent downstream nodes. With this performance information, together with pipeline priorities, the sending node (or reactive node) adjusts the transmission rate and intermediate buffering of data. In this way, the nodes of the pipeline can dynamically regulate interim data storage to avoid overwhelming the pipeline system with too much data during periods of high latency.

Abstract: A monitor provides run time monitoring of the input values to a signature analyzer having a machine-learning engine to compare statistics measuring the domain of those input values to the domain of input values used in the training set of the machine-learning engine. This comparison yields an indication of whether retraining of the machine-learning engine is required without analysis of the output of the machine-learning engine algorithm or the need for errors to become manifest.

Abstract: An industrial data pipeline architecture described supports annotation of raw data streams into scalable and durable cloud-level storage. A dynamic data mapping process collects industrial data from data tags that have been annotated with metadata indicating that the tags are to be made available for visualization, and connects data from these tags to a data aggregator object of a node-level or edge-level visualization tool. The data aggregator object dynamically adjusts its data collection footprint to include the annotated data tags, which are dynamically associated with the physical layout of the industrial equipment in the visualization. Visualization objects for a specified plant configuration act as data context containers that can be added to a plant visualization panel, and allows the annotated tags to be selected to build visualizations of selected data items.

Abstract: A monitor provides run time monitoring of the input values to a signature analyzer having a machine-learning engine to compare statistics measuring the domain of those input values to the domain of input values used in the training set of the machine-learning engine. This comparison yields an indication of whether retraining of the machine-learning engine is required without analysis of the output of the machine-learning engine algorithm or the need for errors to become manifest.

Abstract: A reactive buffering system for use in IIoT data pipelines dynamically adjusts data accumulation and delivery by a node of a pipeline based on aggregated downstream metrics representing current data processing latencies of downstream nodes. Based on these downstream performance metrics, a reactive node that adjusts the size of the next data batch to be sent to an adjacent downstream node. The nodes of the data pipeline are configured to support a request-response based handshaking protocol whereby the nodes that send data to downstream nodes maintain up-to-date performance level information from adjacent downstream nodes. With this performance information, together with pipeline priorities, the sending node (or reactive node) adjusts the transmission rate and intermediate buffering of data. In this way, the nodes of the pipeline can dynamically regulate interim data storage to avoid overwhelming the pipeline system with too much data during periods of high latency.