Aero-engine fault diagnosis method based on transferable neural network

Abstract

The invention provides an aero-engine fault diagnosis method based on a transferable neural network. By using a basic thought of transfer learning, the method overcomes an over-idealized hypothesis problem that training data and a test data set obey the same distribution in a traditional aero-engine gas path fault diagnosis method based on data-driven. Two fault diagnosis algorithms UD-RVFL and UJD-RVFL are provided by combining a domain self-adaption thought and an RVFL algorithm, so as to realize that on the premise that the main advantage that an original RVFL network topology structure is simple is reserved, the distribution difference between data of a source domain and data of a target domain can be reduced by learning feature representation of migratable data, and data attributes and feature structures of the source domain are reserved as much as possible. According to the invention, a transfer learning strategy is adopted, edge distribution and conditional distribution differences existing between data sets can be reduced, and therefore, the fault diagnosis precision is improved.

Description

technical field [0001] The invention belongs to the field of aeroengine fault diagnosis, in particular to an aeroengine fault diagnosis method based on a transferable neural network. Background technique [0002] As one of the effective components of the engine health management system, the aeroengine fault diagnosis system has always been a hot spot in the industry and academia, and the failure probability of engine gas circuit components can account for more than 90% of the overall engine failure, so the establishment of An effective method for fault diagnosis of gas circuit components is particularly important. Currently, available methods for engine fault diagnosis mainly focus on model-based methods and data-driven methods. The model-based method mainly establishes a mathematical model of the engine based on the real engine operating conditions to judge the engine health. This method requires researchers to be very familiar with the working principle of the engine. How...

AI Technical Summary

Problems solved by technology

[0003] However, most studies mainly adopt traditional machine learning theory to solve the problem of fault diagnosis, which may be impractical in practical application scenarios

Usually, these diagnostic methods based on traditional machine learning (including popular deep learning) often assume that the training data set and the test data set obey the same data distribution, and this assumption is too ideal in practical applications

In addition, for those diagnostic algorithms that require a large amount of data, the huge human and time costs of labeling data are often unbearable

Therefore, although these algorithms may have achieved satisfactory diagnostic accuracy, the underlying idealized assumptions are likely to keep them only in well-designed experimental environments, rather than landing in actual engineering application scenarios

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