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: 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: 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 cloud-based global alarm annunciation broker assist in locating and contacting suitable technical support personnel in response to detected alarm events at a plant facility. The system comprises a cloud-based framework that dynamically matches on-site alarm events to domain experts capable of addressing the alarm events. The framework uses an agent-based architecture to gather industrial data from data sources within the industrial environment, including time-series alarm data. The data is received at a cloud platform, where broker services perform a global search for suitable technical support resources in response to alarm events identified by the collected data. The brokering system can automatically generate service tickets and send notifications to both end users and system managers/supervisors, and can notify application-level experts about events and anomalies that emerge from the on-premise processes being monitored by the cloud-based broker system.

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 hybrid data collection and analysis infrastructure combines edge-level and cloud-level computing to perform high-level monitoring and control of industrial systems and processes. Edge devices located on-premise at one or more plant facilities can collect data from multiple industrial devices on the plant floor and perform local edge-level analytics on the collected data. In addition, the edge devices maintain a communication channel to a cloud platform executing cloud-level data collection and analytic services. As necessary, the edge devices can pass selected sets of data to the cloud platform, where the cloud-level analytic services perform higher level analytics on the industrial data. The hybrid architecture operates in a bi-directional manner, allowing the cloud-level and edge-level analytics to send control instructions to industrial devices based on results of the edge-level and cloud-level analytics.

Abstract: A cloud agent facilitates collection of industrial data from one or more data sources on the plant floor and migration of the collected data to a cloud platform for storage and processing. Collection services associated with the cloud agent perform on-premise data collection of historical, live, and/or alarm data directly from industrial devices networked to the agent or from intermediate data concentrators that gather the data from the devices. Queue processing services executed by the cloud agent package the data into a data packet comprising header information that identifies a customer associated with the industrial enterprise, processing priority information, and other information that informs data processing services on the cloud platform how to process and/or direct the incoming data. The cloud agent then establishes a communication channel to the cloud platform and sends the data via the channel.

Abstract: A hybrid data collection and analysis infrastructure combines edge-level and cloud-level computing to perform high-level monitoring and control of industrial systems and processes. Edge devices located on-premise at one or more plant facilities can collect data from multiple industrial devices on the plant floor and perform local edge-level analytics on the collected data. In addition, the edge devices maintain a communication channel to a cloud platform executing cloud-level data collection and analytic services. As necessary, the edge devices can pass selected sets of data to the cloud platform, where the cloud-level analytic services perform higher level analytics on the industrial data. The hybrid architecture operates in a bi-directional manner, allowing the cloud-level and edge-level analytics to send control instructions to industrial devices based on results of the edge-level and cloud-level analytics.

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 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 analytics system streams industrial data from customer facilities to a cloud platform as torrential data streams, and performs analytics on the data contained in the data streams based on a selected set of rules. The rules can be designed to diagnose current or potential issues, to monitor for alarm conditions, or to perform other types of analytics. One or more data pipelines migrate data from plant facilities to a data lake residing on the cloud platform. Data streams can be segregated according to customer, and can further be segregated according to plant facility, production area, or any other suitable classification. Each data stream has an associated manifest that identifies the set of rules to be used to process data in each data stream, allowing selected rules to be applied to each data stream in an ad hoc manner.

Abstract: A cloud-based global alarm annunciation broker assist in locating and contacting suitable technical support personnel in response to detected alarm events at a plant facility. The system comprises a cloud-based framework that dynamically matches on-site alarm events to domain experts capable of addressing the alarm events. The framework uses an agent-based architecture to gather industrial data from data sources within the industrial environment, including time-series alarm data. The data is received at a cloud platform, where broker services perform a global search for suitable technical support resources in response to alarm events identified by the collected data. The brokering system can automatically generate service tickets and send notifications to both end users and system managers/supervisors, and can notify application-level experts about events and anomalies that emerge from the on-premise processes being monitored by the cloud-based broker system.

Abstract: A configuration management interface system is provided for standardizing communication between an external application and one or more hardware or emulated industrial controllers. The interface system reads one or more industrial control programs and presents available data tags defined by the control programs to a user via an interface screen. The user can select a subset of the available data tags that are to be exposed to the external application for data communication. Based on the selected data tags, controller configuration information read from the control programs, and additional configuration information provided by the user, the interface system generates a control unit file that acts as a communication bridge between the external application and the selected data tags residing on the industrial controllers. The control unit file can be used to interface the application (e.g., an industrial simulation or another type of application) with either hardware controllers or emulated controllers.

Abstract: A simulation environment is provided for running a process simulation used to validate an industrial control program. The simulation environment exposes the I/O module configurations defined in the control program and retrieves module configuration information therefrom. This I/O module configuration information is combined with generic, module-specific I/O module profiles to create a pool of available controller I/O points, which can be selectively associated with I/O points in the simulation to create an I/O point mapping. During control program validation, simulated I/O data is exchanged between the process simulation and the I/O module instances in the controller in accordance with the I/O point mapping. A variation of these techniques for use with cloud-based emulations is also described.

Abstract: A cloud-based global alarm annunciation broker assist in locating and contacting suitable technical support personnel in response to detected alarm events at a plant facility. The system comprises a cloud-based framework that dynamically matches on-site alarm events to domain experts capable of addressing the alarm events. The framework uses an agent-based architecture to gather industrial data from data sources within the industrial environment, including time-series alarm data. The data is received at a cloud platform, where broker services perform a global search for suitable technical support resources in response to alarm events identified by the collected data. The brokering system can automatically generate service tickets and send notifications to both end users and system managers/supervisors, and can notify application-level experts about events and anomalies that emerge from the on-premise processes being monitored by the cloud-based broker system.

Abstract: A control loop tuning system executing on a cloud platform facilitate remote control system analysis and generation of suitable controller gains for a given closed-loop control application. The system leverages cloud-side analytics and a gain correlation model generated based on historical data collected from the industrial control system and maintained on cloud storage. The gain correlation model creates a virtual association between controller gains and process variables based on operational and configuration data collected from the industrial control system. The system then applies iterative analytics to the model to converge on a set of controller gains determined to satisfy an optimization criterion. The recommended controller gains are then provided to a client device for review and implementation in the real system controller.

Abstract: A simulation environment for running a process simulation used to validate an industrial control program. The simulation environment exposes the I/O module configurations defined in the control program and retrieves module configuration information therefrom. This I/O module configuration information is combined with generic, module-specific I/O module profiles to create a pool of available controller I/O points, which can be selectively associated with I/O points in the simulation to create an I/O point mapping. During control program validation, simulated I/O data is exchanged between the process simulation and the I/O module instances in the controller in accordance with the I/O point mapping.

Abstract: Systems and methods that efficiently simulate controlled systems are presented. A simulation management component (SMC) controls simulation of a controlled system by controlling a desired number of nodes, each comprising a controller (e.g., soft controller) and a simulated component or process, which are part of the controlled system. The simulation can be performed in a step-wise manner, wherein the simulation can comprise a desired number of steps of respectively desired lengths of time. For each step, the SMC dynamically selects a desired clock (e.g., currently identified slowest clock) as a master clock for the next step. The SMC predicts a length of time of the next step to facilitate setting a desired length of time for the next step based in part on the predicted length of time. As part of each step, components can synchronously exchange data via intra-node or inter-node connections to facilitate simulation.