An evaluation value prediction unit predicts an evaluation value at a time point after a given time passes from a predetermined evaluation time based on input data related to an operation of a factory at the evaluation time using a learned prediction model. The prediction model is a learned model that is learned so that the evaluation value related to the operation of the factory at a time point after the given time passes from one time point is output by inputting a plurality of kinds of data related to the operation of the factory at one time point. An evaluation value output unit outputs information related to the evaluation value.

A board production management device configured to manage a board production line including an initial notification section configured to issue notification information including at least one of a contents of an error cause or a contents of countermeasures when the error cause that requires countermeasures occurs at the board production line; and an escalation notification section configured to issue the notification information after escalating in a case in which the error cause is not resolved after a specified time has elapsed since the error cause occurred. Accordingly, because when the specified time has elapsed, a notification is issued as escalating the notifyee or escalating the contents of countermeasures to resolve error causes more effectively, and compared to conventional technology in which only re-notification of the occurrence of the error cause is performed, notification is performed more efficiently, enabling an error cause that requires countermeasures to be resolved more quickly.

Secure data transmission between an input device and both industrial controllers in a high-availability system utilizes a secure connection established between the primary industrial controller and the input device. Data required to establish the secure connection is stored on the primary controller as part of the connection data corresponding to the secure connection. The input device transmits data to the primary controller over the secure connection according to the desired level of security. The primary controller transmits the connection data defining the secure connection to the secondary controller. If a failure occurs in the primary controller, the secondary controller establishes a connection to the input device using the connection data for the secure connection, such that the secondary controller may assume responsibility for the controller end of the secure connection. The primary controller transmits the input signals to the secondary controller via the dedicated connection between controllers.

A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.

A user apparatus retrieves a list of current features in an industrial automation device in an industrial site, and a list of available features for the industrial automation device, and displays the lists to a user of the user apparatus. If a user input indicating that the industrial automation device is to be updated with one or more features selected amongst the available features, the one or more features are downloaded from a remote data storage to the user apparatus and therefrom to the industrial automation device to update the industrial automation device.

A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.

Provided in an embodiment of the present disclosure is a method for acquiring data of a data source associated with a production line, including: acquiring a semantic model, the semantic model including semantic relationships between respective semantic units and data source identifiers corresponding to one or more production lines; receiving production line identifiers and acquiring one or more semantic units; converting, based upon the semantic model, the one or more semantic units to data source identifiers corresponding to production lines indicated by the production line identifiers; and acquiring data of data sources indicated by the data source identifiers. Implementing the embodiments disclosed in the present disclosure simplifies a configuration file required for a virtual model of a production line, thereby greatly reducing configuration workload, and enhancing convenience of acquiring data of a data source.

A system includes a first asset disposed at a first location in an industrial system configured to perform one or more operations, a second asset disposed at a second location in the industrial system, and a server device communicatively coupled to the first asset and the second asset. The server device is configured to receive a first set of stream-based data from the first asset, receive a second set of stream-based data from the second asset, identify a first portion of the first set of stream-based data and a second portion of the second set of stream-based data, wherein the first portion and the second portion comprises a same type of data, determine whether the first portion of the first set of stream-based data corresponds to an expected range of values based on the second portion of the second set of stream-based data, and send a command to the first asset to adjust a first set of operations in response to the first portion of the first set of stream-based data not being within the expected range of values.

A model-based industrial security policy configuration system implements a plant-wide industrial asset security policy in accordance with security policy definitions provided by a user. The configuration system models the collection of industrial assets for which diverse security policies are to be implemented. An interface allows the user to define zone-specific security configuration and event management policies for a plant environment at a high-level based on a security model that groups the industrial assets into security zones. When new industrial devices are subsequently installed on the plant floor, the system determines whether a security policy defined by the model is applicable to the new device and commissions the new device to comply with any relevant security policies. This mitigates the necessity for a system administrator to manually configure individual devices to comply with plant-wide security policies.

To provide a trusted, secure, and immutable record of storage operations executed by a storage center for storing measurement data provided by a process plant, techniques are described for utilizing a distributed ledger. When a data contributor such as a process plant generates measurement data, an encrypted version of a set of measurement data is transmitted to a storage center for secure storage of the measurement data. In some instances, the data contributor divides the set of measurement data into several subsets and transmits each subset of encrypted measurement data to a different storage center. Furthermore, the storage center generates a transaction for the storage operation which is recorded in a distributed ledger. When a data subscriber retrieves the encrypted measurement data from a storage center, the data subscriber can verify the authenticity of the data based on the information recorded in the distributed ledger.