Civil aviation engine gas path fault diagnosis method based on CNN and SVM

Abstract

The invention relates to a civil aviation engine gas path fault diagnosis method based on CNN and SVM. The method comprises the following steps of acquiring civil aviation engine gas path state data;constructing a training set and a test set; using the training set to train a CNN model; using the trained CNN model to carry out characteristic mining on a sample in the test set to form a test sample characteristic set; using the test sample characteristic set to train the SVM and classifying various faults; and inputting the civil aviation engine gas path state data to be diagnosed into the trained CNN model to obtain a sample characteristic to be tested, and using the SVM to classify to obtain a gas path fault type. In the invention, a convolutional neural network is used to directly process a matrix, the relationship of input parameters and time is considered, and a relationship between the input parameters is considered too. The SVM is used to classify, a limitation that civil aviation engine fault samples are insufficient is well solved, and civil aviation engine gas path fault diagnosis can be effectively and accurately realized.

Description

technical field [0001] The invention relates to the technical field of civil aviation engines, in particular to a method for diagnosing gas path faults of civil aviation engines based on CNN and SVM. Background technique [0002] The gas path state data of civil aviation engines is a typical multi-dimensional time series data. When performing fault diagnosis on the gas path of civil aviation engines, it is necessary to synthesize the changing trends of various monitored gas path performance parameters in order to obtain more accurate fault diagnosis. Through the customer notification report (Customer Notification Report, CNR) fed back to the airline by the civil aviation engine manufacturer (Original Equipment Manufacturer, OEM), it can be found that the OEM manufacturers mainly use the exhaust gas temperature change (Delta Exhaust Gas Temperature, DEGT), The four performance parameters of Exhaust Gas Temperature Margin (EGTM), core engine speed change (Delta Core Speed, DN2...

AI Technical Summary

Problems solved by technology

[0011] The technical problem to be solved by the present invention is to solve the problem that the machine learning fault diagnosis method in the prior art needs to serialize the gas path state data when diagnosing the gas path fault of the civil aviation engine, thereby losing the influence of the mutual relationship between the parameters of the civil aviation engine, resulting in The problem of inaccurate judgment of the status of civil aviation engines and the lack of samples of civil aviation engine failures make it difficult to directly use the CNN method to diagnose civil aviation engine gas path failures

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