Systems and methods for human-machine interaction. An adaptive behavioral control system of a human-machine interaction system controls an interaction sub-system to perform a plurality of actions for a first action type in accordance with a computer-behavioral policy, each action being a different alternative action for the action type. The adaptive behavioral control system detects a human reaction of an interaction participant to the performance of each action of the first action type from data received from a human reaction detection sub-system. The adaptive behavioral control system stores information indicating each detected human reaction in association with information identifying the associated action. In a case where stored information indicating detected human reactions for the first action type satisfy an update condition, the adaptive behavioral control system updates the computer-behavioral policy for the first action type.

A controllable device, such as a set top box, responds to a transmission received from a one of a plurality of controlling devices of differing capabilities by entering into a one of a plurality of operating modes wherein the one of the plurality of operating modes entered into corresponds to the capabilities of the controlling device from which the transmission originated.

Device, Computing Platform and Method of Analyzing Log Files of an Industrial Plant are disclosed. The method including: determining at least one block in log entries of the log files, wherein the log entries includes one or more log messages and wherein the block represents co-occurring log messages; annotating the co-occurring log messages of the block using semantic metadata, wherein the semantic metadata defines one or more message types for the co-occurring log messages, wherein the semantic metadata is indicative of at least one of a start action, an end action, a source, an anomaly, a cause and an inspect action; generating a coherent representation for the block by representing the co-occurring log messages in a graph based on the semantic metadata; and enabling detection of at least one event in the block based on a comparison the coherent representation with template representations of predefined events associated with the industrial plant.

A test system is includes a management server which is configured to provide predefined test instructions, a monitoring system, and at least one execution entity. The monitoring system is configured to convert test instructions provided by the management server into operating instructions for setting a test configuration on a control unit of a system using predefined assignment logic. The at least one execution entity is configured to set the test configuration on the control unit of the system on the basis of operating instructions transmitted by the monitoring system to the at least one execution entity.

Systems and methods for providing operator variation analysis for an industrial operation are disclosed herein. In one aspect of this disclosure, a method for providing operator variation analysis includes processing input data received from one or more data sources to identify transient or non-steady state process data relating to the industrial operation and selecting one or more types of data in the transient or non-steady state process data to cluster for operator variation analysis. The one or more types of data are clustered using one or more data clustering techniques, and the clustered one or more types of data are analyzed to identify a best operator of a plurality of operators responsible for managing the industrial operation. Information is analyzed to determine if one or more gaps exist in the economic operation of the industrial operation due to operator variability between the best operator and other operators.

Systems and methods for providing operator variation analysis for an industrial operation are disclosed herein. In one aspect of this disclosure, a method for providing operator variation analysis includes processing input data received from one or more data sources to identify steady state process data relating to the industrial operation and selecting one or more types of data in the steady state process data to cluster for operator variation analysis. The one or more types of data are clustered using one or more data clustering techniques, and the clustered one or more types of data are analyzed to identify a best operator of a plurality of operators responsible for managing the industrial operation. Information is analyzed to determine if one or more gaps exist in the economic operation of the industrial operation due to operator variability between the best operator and other operators.

Systems and methods for addressing gaps in an industrial operation due to operator variability are disclosed herein. In one aspect of this disclosure, a method for addressing gaps in an industrial operation due to operator variability includes processing input data received from one or more data sources to identify a best operator of a plurality of operators responsible for managing the industrial operation, and determining if one or more gaps exist in the economic operation of the industrial operation due to operator variability between the best operator and operators other than the best operator. Identified gaps may be analyzed to determine if relevant characteristics associated with the gaps justify at least one solution for addressing the gaps for the particular industrial operation. At least one solution may be identified and mapped to the gaps in some instances.

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.

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.