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 safety relay configuration system for configuring safety functions to be carried out by a safety relay is provided. The configuration system comprises a number of features that facilitate intuitive and simplified configuration of an industrial safety relay, including but not limited to features that guide the user through the configuration process using an intuitive sequential procedure that provides feedback and prompts based on user interaction, enforce design consistency throughout the configuration project by intelligently limiting user selections, and visually organize configuration and status information in a manner that efficiently utilizes display space and allows the user to quickly evaluate available configuration options.

A safety relay configuration system for configuring safety functions to be carried out by a safety relay is provided. The configuration system comprises a number of features that facilitate intuitive and simplified configuration of an industrial safety relay, including but not limited to features that guide the user through the configuration process using an intuitive sequential procedure that provides feedback and prompts based on user interaction, enforce design consistency throughout the configuration project by intelligently limiting user selections, and visually organize configuration and status information in a manner that efficiently utilizes display space and allows the user to quickly evaluate available configuration options.

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.