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 cloud human-machine interface (HMI) generation system converts an original HMI project to a web-compatible version of the HMI capable of storage and execution on a cloud platform or web server. The cloud HMI generation system parses HMI project files exported from the original HMI to identify graphical objects and their attributes, animations, data tags linked to the HMI, and other characteristics of the HMI. The system then generates scripts or function calls that reproduce the identified graphical objects and their associated animations, yielding a web-based HMI that emulates the original HMI project. The resulting cloud HMI can retrieve copies of the original industrial system data maintained on cloud-based storage, allowing the cloud HMI to display near real-time system data on a client device from any location.

Abstract: A cloud gateway device 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. The cloud gateway device collects historical, live, and/or alarm data directly from industrial devices networked to the gateway device, formats and encapsulates the data with customer-specific header information, and sends the resulting data packets to a cloud platform for processing by cloud-level analytics services. To keep local memory requirements low, the cloud gateway device supports store-and-forward functionality in the event of a communication loss to the cloud platform, whereby the collected industrial data is redirected to an external data storage repository until communication to the cloud platform is reestablished.

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: Cloud-based data processing services facilitate collection and processing of industrial data in a cloud platform. On-premise data collection agents collect and pre-process industrial data from one or more data sources, including industrial devices, historians, etc. The agents apply a header to the data defining a hierarchical, customer-specific data model that can be leveraged in the cloud platform to suitably process the data. Cloud-side data process services receive the resulting data packets, assign the data to one or more priority queues, and invoke a manifest assembly corresponding to the data model defined by the header. The manifest assembly defines one or more operations to be performed on the received data, including specifying a final storage destination for the data, determining one or more metrics for an industrial system or process based on the received data, or other such operations.

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 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 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: Systems, methods, and apparatuses are provided for a “path affinity” engine that may work in the background to discover route information needed to connect industrial control components to control level data using “tag names.” Connections may be established dynamically through the path affinity engine, thereby simplifying the commissioning process and allowing connections to be made on an as needed basis. Accordingly, discovery of tag locations at runtime may be permitted, and implementing hardware changes in industrial control environments may be greatly simplified.

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 cloud-based multi-tier cyber analytics system is provided for integration of cloud-side and on-premise analytics for industrial systems. The analytics system includes an emulation runtime engine that executes a virtualized controller on a cloud platform. The runtime engine serves as a core analytics component by providing a control-level analytics engine with application programming interfaces (APIs) that enable seamless interaction of distributed simulations, cloud level services, and hardware industrial controllers. A cloud-based framework integrates soft control, hard control, and simulation with cloud-level services, and includes components that facilitate near real-time data streaming from the plant floor to the cloud platform to yield an industrial Internet of Things (IoT).

Abstract: A cloud-based multi-tier cyber analytics system is provided for integration of cloud-side and on-premise analytics for industrial systems. The analytics system includes an emulation runtime engine that executes a virtualized controller on a cloud platform. The runtime engine serves as a core analytics component by providing a control-level analytics engine with application programming interfaces (APIs) that enable seamless interaction of distributed simulations, cloud level services, and hardware industrial controllers. A cloud-based framework integrates soft control, hard control, and simulation with cloud-level services, and includes components that facilitate near real-time data streaming from the plant floor to the cloud platform to yield an industrial Internet of Things (IoT).

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 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: Cloud-based data processing services facilitate collection and processing of industrial data in a cloud platform. On-premise data collection agents collect and pre-process industrial data from one or more data sources, including industrial devices, historians, etc. The agents apply a header to the data defining a hierarchical, customer-specific data model that can be leveraged in the cloud platform to suitably process the data. Cloud-side data process services receive the resulting data packets, assign the data to one or more priority queues, and invoke a manifest assembly corresponding to the data model defined by the header. The manifest assembly defines one or more operations to be performed on the received data, including specifying a final storage destination for the data, determining one or more metrics for an industrial system or process based on the received data, or other such operations.

Abstract: A cloud human-machine interface (HMI) generation system converts an original HMI project to a web-compatible version of the HMI capable of storage and execution on a cloud platform or web server. The cloud HMI generation system parses HMI project files exported from the original HMI to identify graphical objects and their attributes, animations, data tags linked to the HMI, and other characteristics of the HMI. The system then generates scripts or function calls that reproduce the identified graphical objects and their associated animations, yielding a web-based HMI that emulates the original HMI project. The resulting cloud HMI can retrieve copies of the original industrial system data maintained on cloud-based storage, allowing the cloud HMI to display near real-time system data on a client device from any location.

Abstract: A cloud gateway device 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. The cloud gateway device collects historical, live, and/or alarm data directly from industrial devices networked to the gateway device, formats and encapsulates the data with customer-specific header information, and sends the resulting data packets to a cloud platform for processing by cloud-level analytics services. To keep local memory requirements low, the cloud gateway device supports store-and-forward functionality in the event of a communication loss to the cloud platform, whereby the collected industrial data is redirected to an external data storage repository until communication to the cloud platform is reestablished.

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: Systems, methods, and apparatuses are provided for a “path affinity” engine that may work in the background to discover route information needed to connect industrial control components to control level data using “tag names.” Connections may be established dynamically through the path affinity engine, thereby simplifying the commissioning process and allowing connections to be made on an as needed basis. Accordingly, discovery of tag locations at runtime may be permitted, and implementing hardware changes in industrial control environments may be greatly simplified.