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.

In a Boundaryless Control High Availability (“BCHA”) system (e.g., industrial control system) comprising multiple computing resources (or computational engines) running on multiple machines, technology for computing in real time the overall system availability based upon the capabilities/characteristics of the available computing resources, applications to execute and the distribution of the applications across those resources is disclosed. In some embodiments, the disclosed technology can dynamically manage, coordinate recommend certain actions to system operators to maintain availability of the overall system at a desired level. High Availability features may be implemented across a variety of different computing resources distributed across various aspects of a BCHA system and/or computing resources. Two example implementations of BCHA systems described involve an M:N working configuration and M:N+R working configuration.

A system and method includes downloading an asset specification of a field device to an edge device, the field device configured to generate data relating to an asset of an industrial environment, the edge device located in the industrial environment, wherein the edge device is configured to receive the data generated by the field device; receiving an asset monitoring sub model for the field device, wherein the asset monitoring sub model specifies an interface specification and algorithm of an asset monitoring application for the field device; deploying the asset monitoring application for the field device to the edge device, wherein the deployment comprises utilization of the asset monitoring sub model; and generating a workflow function exposed by the asset monitoring application, wherein the generation of the workflow function comprises utilization of the asset specification of the field device and the interface specification of the asset monitoring sub model.

A method for correlating data from sensors includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from component sensors is used for functionality of a component of the industrial operation and sensor information from additional sensors monitor conditions of a portion of the industrial operation different from the component. The method includes deriving, using the sensor information, correlations between component sensors and additional sensors and deriving a baseline signature from the sensor information and the correlations. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition based on a comparison between additional sensor information and the baseline signature. The sensor information is used differently for functionality of the component than for deriving the correlations and baseline signature and identifying the abnormal operating condition. The method includes sending an alert with the abnormal operating condition.

A system may include a control system for controlling one or more operations of one or more industrial devices in an industrial system. The control system may receive streaming data comprising one or more visualizations representative of one or more live operational parameters associated with one or more industrial devices. The streaming data may include multiple image frames. The control system may also identify multiple datasets associated with the streaming data and generate multiple machine-readable images based on the multiple datasets. In addition, the control system may embed the multiple machine-readable images within the multiple image frames of the streaming data to generate updated streaming data and send the updated streaming data to a computing system that may extract the multiple machine-readable images from the updated streaming data.

Embodiments of the present disclosure provide industrial control apparatus and monitoring methods for industrial control apparatus. The industrial control apparatus comprises: at least one input or output module, each input or output module comprising a fuse adapted to be coupled between a power supply and a user load, and a detection assembly coupled to the fuse and configured to send a detection signal after monitoring that the fuse is blown; and a processing device communicatively coupled to the detection assembly of each input or output module and configured to output information that the input or output module associated with the detection signal is blown. By the aspects of the present disclosure, it is advantageous for an operator to timely obtain information that the fuse is blown and to improve the efficiency of locating and replacing the fuse.

The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.

Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings. The resulting aligned batch data of the subject industrial process enables improved modeling and control of batch productions by the subject industrial process.

A computer-implemented method of optimizing an industrial process includes comparing current environmental condition data to historic environment condition data for at least one day preceding a specified day. The method also includes determining a visual state from a plurality of visual states for the at least one day based on the comparison between the current environmental condition data and the historic environment condition data. The method further includes generating a calendar interface comprising a plurality of days preceding the specified day and corresponding to a plurality of visual representations. The method further includes generating a graphical user interface comprising historical data for at least one type of industrial process.

A method of monitoring an industrial plant, implemented by an apparatus, includes receiving operational data from each of a plurality of electric drives via a corresponding communication interface. The method further includes converting the operational data from a pre-determined data format to a machine-readable data format and determining a plurality of events for each of the plurality of drives based on the corresponding machine-readable data. The method includes determining a plurality of critical events corresponding to the plurality of drives based on the plurality of events and generating a recommendation based on at least one of the plurality of critical events. The method also includes presenting the recommendation on an output device in a human readable format.