A method of controlling a complex system and a gas turbine being controlled by the method are provided. The method comprises providing training data, which training data represents at least a portion of a state space of the system; setting a generic control objective for the system and a corresponding set point; and exploring the state space, using Reinforcement Learning, for a control policy for the system which maximizes an expected total reward. The expected total reward depends on a randomized deviation of the generic control objective from the corresponding set point.

A method for operating a fan system as well as such a fan system. The fan system has a control device having an artificial neural network. The control device controls an electric motor of a backward curved centrifugal fan. The centrifugal fan creates a gas flow that is characterized by an actual flow value, particularly the actual value of a volume flow rate. The actual flow value is not detected by a sensor means, but determined by means of the artificial neural network depending from input variables and based thereon, the electric motor is open loop or closed loop controlled by means of the control device. The motor current and the motor voltage as well as their time-dependent behavior that can be the time derivative (e.g. gradient of first order) or that can be at least one preceding value at a preceding point in time, are provided to an input layer of the artificial neural network. It is particularly advantageous, if the artificial neural network determines an actual value of an output pressure that is fed back internally or externally forming an input variable for the input layer.

A computer-implemented method comprising: controlling input of data quantifying damage received by a compressor of a gas turbine engine into a first machine learning algorithm; receiving data quantifying a first operating parameter of the compressor as an output of the first machine learning algorithm; and determining operability of the compressor by comparing the received data quantifying the first operating parameter of the compressor with a threshold.

A method for fault identification for a gas turbine engine includes receiving sensor data from at least one health monitoring sensor of a health monitoring system for a gas turbine engine; utilizing a pre-filter to filter the sensor data and obtain filtered data based on a plurality of signatures of fault conditions of the health monitoring system and the gas turbine engine; utilizing at least one transfer function to extract features from the filtered sensor data based on the plurality of signatures; utilizing a machine learning technique to analyze the extracted features, and determine whether any of the extracted features are indicative of a fault condition in the health monitoring system or the gas turbine engine; and based on the analysis indicating the presence of a fault condition, providing a fault detection notification. A health monitoring system for a gas turbine engine is also disclosed.

A computer-implemented method controls input of at least a portion of a first training data set into a first machine learning algorithm. The first training data set includes data quantifying damage to a first compressor and data quantifying a first operating parameter of the first compressor. The first machine learning algorithm is executed, and data quantifying the first operating parameter is received as an output of the first machine learning algorithm. The first machine learning algorithm is trained using the received data output from the first machine learning algorithm and data quantifying the first operating parameter of the first compressor. The trained first machine learning algorithm is configured to enable determination of operability of a second compressor of a gas turbine engine.

Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods use machine learning (ML) based analytics on an edge device directly at the well site to detect possible occurrence of abnormal events and automatically respond to such events. The event detection may be based on trends identified in the data acquired from the well site operations in real time. The trends may be identified by correlation and by fitting line segments to the data and analyzing the slopes of the line segments. Upon detecting unusual event, the edge device can issue alerts regarding the event and take predefined steps to reduce potential damage resulting from such event. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

A vacuum pump inlcudes a housing having an inlet and an outlet, at least one rotor arranged in the housing configured to convey a gaseous medium from the inlet to the outlet, a motor configured to rotate the rotor, a control device connected to the motor configured to control the motor, and at least one sensor connected to the control device. The at least one sensor is configured to sense at least one operating parameter of the vacuum pump. The control device comprises a correlation module. The correlation module is configured to correlate the sensed at least one operating parameter with at least one critical parameter. The motor is controlled on the basis of the at least one critical parameter.

Computer-implemented methods are disclosed for determining a flow rate of fluid flow at a target time through a pump, such as a centrifugal pump, the pump being driven by a pump motor. An illustrative method includes, in some aspects: receiving at least one set of previous parameter values including a first previous parameter value indicative of a first operational parameter of the pump motor at a previous time, earlier than the target time, and a second previous parameter value indicative of a second operational parameter of the pump motor at the previous time, and receiving a current set of parameter values including a first current parameter value indicative of the first operational parameter of the pump motor at the target time and a second current parameter value indicative of the second operational parameter of the pump motor at the target time.

Examples described herein provide a computer-implemented method that includes receiving training data indicative of incipient compressor surge for cabin air compressors. The method further includes generating, using the training data, a training spectrogram. The method further includes training, by a processing system, a machine learning model to detect incipient compressor surge events for the cabin air compressors using the spectrogram. The method further includes receiving, at a microcontroller associated with a cabin air compressor, operating data associated with the cabin air compressor. The method further includes generating, at the microcontroller and using the operating data, an operating spectrogram. The method further includes detecting, by the microcontroller associated with the cabin air compressor, an incipient compressor surge event by applying the machine learning model to the operating spectrogram. The method further includes implementing a corrective action to correct the incipient compressor surge event.

A computer-implemented method comprising: controlling input of at least a portion of a first training data set into a first machine learning algorithm, the first training data set including: data quantifying damage to a first compressor; and data quantifying a first operating parameter of the first compressor; executing the first machine learning algorithm; receiving data quantifying the first operating parameter as an output of the first machine learning algorithm; and training the first machine learning algorithm using: the received data output from the first machine learning algorithm; and data quantifying the first operating parameter of the first compressor, the trained first machine learning algorithm being configured to enable determination of operability of a second compressor of a gas turbine engine.