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, 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 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: 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 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: 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: Data flows and data processing modules are provided to fulfill the implementation of: contextualized data collection, scalable analytics, and machine learning operations quality assurance. These modules may be implemented as standalone components, and/or bundled as a group of coordinated microservices. The conversion of raw domain expertise and knowledge into data processors, analytics, and visualization modules for industry oriented Industrial Internet of Things (IIoT) solutions is streamlined.

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: 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: Systems and method 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 devise, the anomaly detection system may generate a social network model that is indicative of expected network communication properties. By analyzing social network metrics and the overall entropy of the network communication in real time, the anomaly detection system may identify anomalies that may be associated with potential network vulnerabilities.