To provide a trusted, secure, and immutable record of transactions within a process plant, techniques are described for utilizing a distributed ledger in process control systems. The distributed ledger may be maintained by nodes which receive transactions broadcasted from field devices, controllers, operator workstations, or other devices operating within the process plant. The transactions may include process plant data, such as process parameter data, product parameter data, configuration data, user interaction data, maintenance data, commissioning data, plant network data, and product tracking data. The distributed ledgers may also be utilized to execute smart contracts to allow machines such as field devices to transact by themselves without human intervention. In this manner, recorded process parameter values and product parameter values may be retrieved to verify the quality of products. Moreover, regulatory data may be recorded in response to triggering events so that regulatory agencies can review the data.

A system for storing data in an industrial production environment includes a distributed database stored on a plurality of intelligent programmable logic controller devices. Each respective intelligent programmable logic controller device includes a volatile computer-readable storage medium containing a process image area; a non-volatile computer-readable storage medium; a control application; an input/output component; a historian component; and a distributed data management component. The control application is configured to provide operating instructions to a production unit. The input/output component is configured to update the process image area during each scan cycle with data associated with the production unit. The historian component is configured to store automation system data including the operating instructions and contents of the process image area on the non-volatile computer-readable storage medium. The distributed data management component is configured to facilitate distributed operations involving the automation system data by the plurality of intelligent programmable logic controller devices.

Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information. The blockchain-enabled industrial devices can be configured to generate multiple versions of a product or machine's blockchain having respective different access permissions, allowing public and private industrial data to be segregated between public and private industrial blockchains.

Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information. The blockchain-enabled industrial devices can be configured to generate multiple versions of a product or machine's blockchain having respective different access permissions, allowing public and private industrial data to be segregated between public and private industrial blockchains.

Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information. The blockchain-enabled industrial devices can be configured to generate multiple versions of a product or machine's blockchain having respective different access permissions, allowing public and private industrial data to be segregated between public and private industrial blockchains.

A distributed big data device in a process plant includes an embedded big data appliance configured to locally stream and store, as big data, data that is generated, received, or observed by the device, and to perform one or more learning analyzes on at least a portion of the stored data. The embedded big data appliance generates or creates learned knowledge based on a result of the learning analysis, which the device may use to modify its operation to control a process in real-time in the process plant, and/or which the device may transmit to other devices in the process plant. The distributed big data device may be a field device, a controller, an input/output device, or other process plant device, and may utilize learned knowledge created by other devices when performing its learning analysis.

A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

A data pipeline is used as a fundamental processing element for implementing techniques that automatically or autonomously perform signal processing-based learning in a process plant or monitoring system. Each data pipeline includes a set of communicatively interconnected data processing blocks that perform processing on one or more sources of data in a predetermined order to, for example, clean the data, filter the data, select data for further processing, perform supervised or unsupervised learning on the data, etc. The individual processing blocks or modules within a data pipeline may be stored and executed at different devices in a plant network to perform distributed data processing. Moreover, each data pipeline can be integrated into one or more higher level analytic modules that perform higher level analytics, such as quality prediction, fault detection, etc. on the processed data. The use of data pipelines within a plant network enables data collected within a plant control or monitoring system to be processed automatically and used in various higher level analytic modules within the plant during ongoing operation of the plant.

A device supporting big data in a process plant includes an interface to a communications network, a cache configured to store data observed by the device, and a multi-processing element processor to cause the data to be cached and transmitted (e.g., streamed) for historization at a unitary, logical centralized data storage area. The data storage area stores multiple types of process control or plant data using a common format. The device time-stamps the cached data, and, in some cases, all data that is generated or created by or received at the device may be cached and/or streamed. The device may be a field device, a controller, an input/output device, a network management device, a user interface device, or a historian device, and the device may be a node of a network supporting big data in the process plant. Multiple devices in the network may support layered or leveled caching of data.

The problem addressed by the invention is to operate an electrical component (16) in a cyber-physical system (22). The adapter device (36) provided according to the invention for coupling the component (16) to a data network (20) of the cyber-physical system (22) comprises: a communication unit (40) which is designed to receive defined request data (24) from the data network (20) independently of the component; an interpretation unit (50) which is designed to determine a command (68) executable using the technical features of the component (16) depending on the request data (24); an assessment unit (52) which is designed to generate a potential solution (70) to the command (68) comprising at least one control signal (32) for the component (16) depending on operating data of the component (16); and a controller (66) which is designed to issue the at least one control signal (32) of the potential solution (70) to a control interface (30) of the component (16).