A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

An automation system, a method and an apparatus for suggesting and/or creating an agent in an industrial automation system that includes automation devices having a framework which is formed to execute the agent and which at least partially includes a data source that collects and/or processes data of the automation devices, and includes a data sink, in which data, in particular status data, of the data sources is saved, wherein an agent suggestion component processes data of the data sink into clusters via a cluster analysis, and wherein the agent suggestion component makes the clusters available at an interface such that a model for the agents becomes creatable by an agent generation component based on at least one selection of the clusters such that it becomes possible to suggest and/or create agents in a simpler and more efficient manner.

An online monitoring system for industrial processes, such as nuclear power processes, including a data acquisition unit configured to sample output signals simultaneously from a plurality of process sensors, and a computing unit configured to record sampled output signals from the data acquisition unit and to cross-correlate the output signals from two or more of the process sensors to diagnose operation of the industrial process, identify loose parts and/or degradation of industrial plant equipment, enable virtual sensing, calculate sensor response time using the noise analysis technique, and to verify sensor calibration using the cross calibration method and/or empirical and/or physical modeling.

An automation system, a method and an apparatus for suggesting and/or creating an agent in an industrial automation system that includes automation devices having a framework which is formed to execute the agent and which at least partially includes a data source that collects and/or processes data of the automation devices, and includes a data sink, in which data, in particular status data, of the data sources is saved, wherein an agent suggestion component processes data of the data sink into clusters via a cluster analysis, and wherein the agent suggestion component makes the clusters available at an interface such that a model for the agents becomes creatable by an agent generation component based on at least one selection of the clusters such that it becomes possible to suggest and/or create agents in a simpler and more efficient manner.

A method for making reliable sensor data available and a device for making reliable sensor data of a system available is provided, including the following steps: receiving sensor data from at least one sensor unit that monitors a system component of the system, and processing the received sensor data using at least one stored ontology and a statistical data analysis model for generating the reliable sensor data.