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 scalable industrial asset management system dynamically negotiates allocation of mobile industrial assets to industrial operation sites. The asset management system tracks and models the capabilities and availabilities of a pool of mobile industrial assets (e.g., truck-mounted assets or other such assets). Based on a defined demand of a scheduled industrial operation requiring mobile industrial assets (e.g., a fracking operation, a mining operation, etc.) the system selects a subset of the mobile industrial assets that are both available during the scheduled operation and are collectively capable of satisfying the demands of the industrial operation. Moreover, based on the asset models for the subset of mobile industrial assets, the system configures an on-premise cloud agent device to collect telemetry data from the mobile assets during the operation and to migrate the collected data to a cloud-based collection and analytics system.

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 scalable industrial asset management system dynamically negotiates allocation of mobile industrial assets to industrial operation sites. The asset management system tracks and models the capabilities and availabilities of a pool of mobile industrial assets (e.g., truck-mounted assets or other such assets). Based on a defined demand of a scheduled industrial operation requiring mobile industrial assets (e.g., a fracking operation, a mining operation, etc.) the system selects a subset of the mobile industrial assets that are both available during the scheduled operation and are collectively capable of satisfying the demands of the industrial operation. Moreover, based on the asset models for the subset of mobile industrial assets, the system configures an on-premise cloud agent device to collect telemetry data from the mobile assets during the operation and to migrate the collected data to a cloud-based collection and analytics system.

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: 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.

Abstract: Described herein are improvements for identifying defects during automated item production. In one example, a method includes identifying a first defect in a first item. The first defect is associated with a stage of production of the first produced item. The method further includes retrieving first parametric data associated with the stage for the first item and identifying one or more defect indicators based on the first parametric data and second parametric data associated with the stage for one or more second items having defects associated with the stage. The method also includes monitoring subsequent parametric data associated with the stage to recognize the one or more defect indicators in the subsequent parametric data.

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 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 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 pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

Abstract: Described herein are improvements for characterizing performance of a component in an automated industrial process. In one example, a method includes, for each period of a plurality of periods, at least until an alert is triggered, updating a count of failed operations attempted by the first component and a percentage of a count of total operations attempted by the first component represented by the count of failed operations. The method further includes decrementing a trigger value when the count of failed operations does not increase for the period and incrementing the trigger value when the count of failed operations increases for the period. Also, the method includes triggering the alert in response to the trigger value satisfying a trigger value criterion and either the count of failed operations satisfying a count criterion or the percentage satisfying a percentage criterion.

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 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 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 manifest generation system generates a system model for a cloud computing architecture. The system generates the system model in the form of system, data, and metrics manifests that act as an information concentrator for configuring various aspects of data ingestion and data management. The manifest generation system leverages both information extracted from industrial devices, applications, and programs that make up physical industrial automation systems, as well as user selections identifying which data tags are to be collected, specifying data collection preferences, etc. In this way, manifest data for configuring cloud-level data monitoring and collection is mapped to the automation and control system configurations via information extracted from the system-level topology. This approach can automate and simplify aspects of the cloud-based data collection configuration process.

Abstract: A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

Abstract: Described herein are improvements for identifying defects during automated item production. In one example, a method includes identifying a first defect in a first item. The first defect is associated with a stage of production of the first produced item. The method further includes retrieving first parametric data associated with the stage for the first item and identifying one or more defect indicators based on the first parametric data and second parametric data associated with the stage for one or more second items having defects associated with the stage. The method also includes monitoring subsequent parametric data associated with the stage to recognize the one or more defect indicators in the subsequent parametric data.

Abstract: Described herein are improvements for characterizing performance of a component in an automated industrial process. In one example, a method includes, for each period of a plurality of periods, at least until an alert is triggered, updating a count of failed operations attempted by the first component and a percentage of a count of total operations attempted by the first component represented by the count of failed operations. The method further includes decrementing a trigger value when the count of failed operations does not increase for the period and incrementing the trigger value when the count of failed operations increases for the period. Also, the method includes triggering the alert in response to the trigger value satisfying a trigger value criterion and either the count of failed operations satisfying a count criterion or the percentage satisfying a percentage criterion.

Abstract: A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.