Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

An industrial integrated development environment (IDE) includes analytic features that generate control project telemetry data based on analysis of a control system design and uses this project telemetry data as the basis for control system design guidance and recommendations. The IDE system can analyze the control system design and identify industrial devices use by the control project, device features that are utilized by the control project, resource utilizations, and other such project telemetry. The IDE system can generate recommendations for improving the control system design based on this project telemetry. The project telemetry data can also be provided to product vendors, offering insights into how the vendors' products are being used by end customers.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving image data after an operation is performed by an industrial automation device on a product; analyzing the image data based an object-based image analysis (OBIA) model to classify the product as one of a plurality of conditions related to manufacturing quality and the OBIA model includes property layers associated with features related to a manufacturing of the product; determining whether the one of the conditions indicates an anomaly being present in the product; sending a notification indicative of the one of the plurality of conditions is presently associated with the product; identifying a property layer associated with classifying the one of the plurality of conditions; and updating the OBIA model based on the property layer and the input indicative of the anomaly being incorrectly associated with the product.

A tangible, non-transitory, computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive user input indicative of a selection of a user experience of a plurality of user experiences. The plurality of user experiences include a first user experience associated with a first event that occurred in an industrial automation system at a first time prior to receiving the user input and a second user experience associated with a second event expected to occur in the industrial automation system at a second time after receiving the user input. When executed, the instructions also cause the processing circuitry to determine, based on the user input, output representative data associated with the industrial automation system and instruct an extended reality device to present the output representative data.

In one embodiment, a non-transitory computer readable medium may include computer-executable instructions that, when executed by a processor, may cause processor to receive a set of user data associated with a user that is attempting to access an electronic lock, receive a request to actuate a locking mechanism of the electronic lock configured to prevent the user from accessing a machine in an industrial automation system, actuate the locking mechanism in response to the request and the set of user data corresponding to an expected set of data, store a log of the request and the set of user data, and send the log to a cloud-based computing system.

Redundancy synchronization of remote terminal unit (RTU) central processing units (CPUs) associated with an industrial operation includes queuing time-stamped events on a main RTU CPU for transfer to a standby RTU CPU as the time-stamped events are generated on the main RTU CPU (i.e., in real-time). The synchronized RTU CPUs further permit synchronization of logic states and synchronization of firmware upgrades. Synchronization activities occur on the same synchronization communications channel between redundant RTU CPUs.

An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.

Disclosed herein are systems and methods for assessing an impact of malicious software causing a denial of service of components of industrial automation and control systems (IACS). In one aspect, an exemplary method comprises, generating a configuration of the IACS on a testing device based on specifications, obtaining a set of investigated software, where the set includes at least one sample of one malicious software, testing the generated configuration using the received set of investigated software, identifying occurrences of denials of service of the components of the testing device which are used to simulate the generated configuration, determining an impact of the malicious software on the generated configuration, and a degree of degradation of a performance of the generated configuration of IACS, and pronouncing a verdict as to a danger of the malicious software for the generated configuration of IACS based on the determined impact of the malicious software.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.