An industrial automation component, a computer program and a computer-readable medium and method for configuring an industrial automation component, wherein at least one feature of the industrial automation component that is not configurable with an engineering system supporting the component, non-supported feature, is configured by interpreting a description of a configuration of the at least one non-supported feature with an on-board compiler of the component and integrating the interpreted description to a basic configuration having been generated with the engineering system and with respect to at least one further feature, supported feature, of the component.

A computer-implemented method for transforming a sequence comprising multiple words from a natural language to a machine executable sequence in real-time to control a machine. The sequence constituted by multiple characters forming words from the natural language is preprocessed by comparing the sequence to data from a database comprising classes. The sequence is searched for simple expressions and GI expressions known to be an upstream function class or a downstream function class. The sequence is dichotomized until all of the function classes contained in the sequence that are capable of resulting in dichotomies have been dichotomized. The first to the last classes of the sequence are iterated and each executable class is executed by a machine.

A method comprises receiving by a control system a sequence of symbols, translating the received sequence of symbols into a batch of commands parsable by an electronic controller of the electromechanical medical device, sending the batch of commands from the control system to the electromechanical device, and causing by the electronic controller the electromechanical medical device to execute the batch of commands on the electromechanical medical device.

A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

A control system, a control method, and a computer readable medium having stored thereon a computer executable program code for controlling electromechanical medical devices is disclosed herein. The method comprises: receiving by a control system a sequence of symbols; translating the received sequence of symbols into a batch of commands parsable by an electronic controller of the electromechanical medical device; sending the batch of commands from the control system to the electromechanical device; and causing by the electronic controller the electromechanical medical device to execute the batch of commands on the electromechanical medical device.

Methods and apparatus to display shift change notes generated via natural language processing models are disclosed. An example apparatus comprises interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to access input data from a workstation in a process control system, the input data associated with a first user account, transmit the input data to a natural language processing (NLP) model, the NLP model to generate a representation of the input data based on stored data associated with the process control system, and display the representation of the input data on a user interface of the workstation after detecting an access of a second user account different from the first user account.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault/failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.