Generally discussed herein are devices, systems, and methods for predicting fan failure. A method includes providing, to a first NN that models nominal behavior of a first fan and provides a value indicating a first amount of deviation from nominal as an output, first parameters of fan operation of the first fan, receiving, from the first NN and responsive to the first parameters, first data indicating the first amount of deviation from nominal, providing, to a second NN that models nominal behavior of a second fan and provides a value indicating a second amount of deviation from nominal as an output, second parameters of fan operation of the second fan, receiving, from the second NN and responsive to the first parameters, second data indicating the second amount of deviation from nominal, and estimating that the first or second fan has failed by comparing the first and second data.

Methods, apparatus, systems, and articles of manufacture are disclosed to autonomously detect thermal anomalies. Disclosed examples include an example apparatus to detect engine anomalies comprising: at least one memory; instructions in the apparatus; and processor circuitry to execute the instructions to: control a plurality of infrared cameras to capture a baseline image set, the baseline image set including at least two thermal images; generate emissivity data based on the baseline image set; provide the baseline image set and the emissivity data to an artificial intelligence model, the artificial intelligence model to generate a reconstructed image set; determine a difference between the baseline image set and the reconstructed image set; and in response to the difference exceeding a threshold, generate an alert indicating detection of an engine anomaly.

A control system for a variable area fan nozzle (VAFN) is disclosed. The VAFN may have a plurality of petals and may be for use with a gas turbine engine. The control system may include a primary system configured to acquire primary data indicative of an operating condition of the VAFN, a secondary system configured to acquire secondary data indicative of a current operating condition of the gas turbine engine, and a control module in operative communication with the primary system and the secondary system. The control module may be configured to: determine a nozzle area of the VAFN based at least in part on the primary data, adjust the determined nozzle area based on the secondary data, and position the plurality of petals according to the adjusted nozzle area.

A method includes obtaining a first test matrix for a first aircraft system and a second test matrix for a second aircraft system. The method also includes, during a first operational test of the first test matrix, obtaining sensor data that includes second sensor data that is not specified by the first test matrix. The method includes evaluating a second operational test of the second test matrix by processing the second sensor data using a second analytic model of the second aircraft system. The method also includes generating second predicted sensor data based on the evaluation of the second operational test. The method includes generating a second error measure by comparing a second subset of the sensor data to the second predicted sensor data. The method includes determining, based at least in part on a range of the second sensor data, a test coverage metric of the second test matrix.

An apparatus for monitoring of a pump includes a control module, and an error detection unit, wherein a support vector machine based module is provided that receives an estimated output quantity data value from the control module, processes the estimated output quantity data value to provide a processed estimated output quantity data value via the support vector machine, and supplies the processed estimated output quantity data value to the error detection unit instead of the estimated output quantity data value of the control module.

Combined cycle gas turbine (CCGT) power plants have become common for generation of electric power due to their high efficiencies. There are various problem related with improving the efficiency of CCGT plants by optimizing the manipulated variables. The method and system for optimizing the operation of a combined cycle gas turbine has been provided. The system is configured to calculate an optimal value of manipulated variables (MV) with efficiency as one of the key performance parameters (KPI). The MVs from the existing CCGT automation system, i.e. a first set of manipulated variables and the manipulated variables from the optimization approach, i.e. a second set of manipulated variables are combined to determine an optimal set of manipulated variables. The method further checks for the anomalous behavior of the system and define the root cause of the identified anomaly and the operational state of the CCGT plant.

A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.

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 system for measuring a first physical parameter includes a mechanical device configured to operate based on the first physical parameter, and a source sensor coupled with the mechanical device and configured to output a signal indicative of the first physical parameter that is based on a second physical parameter and a forward mapping function, wherein the forward mapping function is learned during a system training phase in which pairwise first physical parameter data from a first physical parameter sensor is forward mapped from second physical parameter data from the source sensor.

The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.