A system includes a power generation system and a controller that controls the power generation system. The controller includes a processor that generates a model of the power generation system that estimates a value for a first parameter of the power generation system. The processor also receives a measured value of the first parameter. The processor further adjusts a correction factor of the model such that the estimated value of the first parameter output by the model is approximately equal to the measured value of the first parameter. The processor also generates a transfer function that represents the correction factor as a function of a second parameter of the power generation system. The processor further displays the transfer function along with one or more previously generated transfer functions to quantify degradation of the power generation system.

A method for monitoring an IO link system and/or at least one IO link device of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system is suggested. The current (I.sub.m), the voltage (U.sub.m) and/or the electrical power (P.sub.m) are here recorded (42) at at least one port of an IO link master of the IO link system. A monitoring of a condition (Z) and/or a detection of anomalies, errors, deviations and/or maintenance indicators and/or a prediction of a maintenance requirement, an error and/or an outage of the IO link system and/or of the at least one IO link device and/or of the plant, of the plant part and/or of the process in the IO link master occurs by means of a model (M) for the current, the voltage and/or the electrical power previously learned via machine learning.

A method of identifying anomalous data obtained by at least one sensor of a plurality of sensors located within an environment. The method includes identifying, based on sensor data obtained from the plurality of sensors, at least one instance of a sequence of events that occurred within the environment. A probability of the sequence of events occurring within the environment under non-anomalous conditions is obtained. A frequency characteristic dependent on a frequency at which the sequence of events occurred within the environment is determined. A likelihood of the sequence of events occurring within the environment at the frequency is determined, based on a combination of the probability and the frequency characteristic. It is identified, based on the likelihood, that at least a portion of the sensor data is anomalous.

A vehicle fault detection system including at least one sensor configured for coupling with a vehicle system, a vehicle control module coupled to the at least one sensor, and being configured to receive at least one time series of numerical sensor data from the at least one sensor, at least one of the at least one time series of numerical sensor data corresponds to a respective system parameter of the vehicle system being monitored, generate a graphical representation for the at least one time series of numerical sensor data to form an analysis image of at least one system parameter, and detect anomalous behavior of a component of the vehicle system based on the analysis image, and a user interface coupled to the vehicle control module, the user interface being configured to present to an operator an indication of the anomalous behavior for the component of the vehicle system.

A monitoring system includes a real-time detection device configured to detect a state of target equipment and an instruction output from a central control device configured to input a control instruction to the target equipment; and a monitoring device configured to acquire information from the real-time detection device. The monitoring device includes an analysis unit configured to simulate the state of the target equipment, with models of the target equipment and the central control device, and a determination unit configured to determine whether an abnormality has occurred in the target equipment. The analysis unit is configured to simulate the state of the target equipment, using the latest information detected by the real-time detection device. The determination unit is configured to compare a result calculated by the analysis unit using the latest information detected by the real-time detection device.

An Artificial Intelligence/Machine Learning driven assessment system for monitoring electrical equipment assets includes a computer system that is configured to receive user-provided asset data associated with operation of a plurality of electrical equipment assets operated by a plurality of users/owners, where the identity of any asset in the database is restricted and only known to the user that owns/operates the asset. The computer system is configured to analyze the user data in conjunction with a pooled knowledge database so as to generate courses of action or assessments for the monitored electrical equipment assets and to update the analysis process based on feedback from a comparison of the assessment or course of action with an actual outcome.

A monitoring system is configured to monitor a property. The system includes a sensor that is configured to generate sensor data that reflects an attribute of the property. The system further includes an HVAC system that is configured to generate and provide conditioned air to the property and that is configured to generate HVAC system data that reflects an attribute of the HVAC system. The system includes a monitor control unit that is configured to determine that the HVAC system is likely malfunctioning. The control unit is configured to receive the sensor data. The control unit is configured to determine that the HVAC system is likely operating correctly. The control unit is configured to determine a cause of the HVAC system transitioning from likely malfunctioning to likely operating correctly. The control unit is configured to update a model that is configured to identify causes of HVAC system malfunctions.

Performance of a decision-making logic (35) of a controller (31) of an industrial automation control system is monitored during field operation of the controller (31). A supervisory system (20) receives operational data collected during field operation of the controller (31). The supervisory system performs an analysis of the operational data to assess performance of the decision-making logic (35), using pre-operational data generated prior to field operation of the controller (31) and/or a performance assessment logic (27) generated prior to field operation of the controller (31). The supervisory system (20) generates an analysis output based on a result of the analysis

A method for determining an emission behaviour of a gas turbine engine. In order to provide a reliable operation of the gas turbine engine the method includes: parameterising the emission behaviour of the gas turbine engine for at least one selected first state variable of the gas turbine engine by using a model, which reflects a state behaviour of the gas turbine engine, and determining the emission behaviour of the gas turbine engine by using the parameterisation.

Disclosed herein a method and machine monitoring system for predicting failures of industrial machines. The system is configured to receive sensor data related to a machine, such as large industrial machinery, and select indicative data features for machine failures. The system then applies an unsupervised machine failure detection process and a supervised machine failure prediction process to the selected indicative data feature. When new sensor data of the machine is received, a machine failure detection process is applied to the selected at least one indicative data feature that is associated with the new sensor data. This allows the disclosed system to determine whether at least one machine failure indicator was detected and if so, the machine failure is tagged. Then, the system updates the supervised machine failure prediction process with the new tagged machine failure indicators, such that the supervised machine failure prediction process is continuously updated and improved.