Aero-engine bearing fault diagnosing method

Abstract

The invention discloses an aero-engine bearing fault diagnosing method. The method comprises: first, acquiring the vibration signal of a turbojet engine, the engine speed, the engine rotation speed, the engine exhaust temperature, the fuel flow, and the engine thrust; extracting the classical time-domain statistical characteristics, the frequency-domain characteristics of the various signals and obtaining the time-frequency domain characteristics based on wavelet packet decomposition; then using the deep Boltzmann machine to perform characteristics learning to the characteristic parameters of the various signals; then, proceeding to use the data integration technology to integrate the characteristics of the various sources learned by the deep Boltzmann machine; and finally, using the integrated characteristic parameters as the input of the classifier to the support vector machine, and classifying and identifying the engine fault. The method of the invention improves the identification precision and stability of the turbojet engine bearing fault, effectively mining the essential characteristics of data and greatly increasing the fault precision of the turbojet engine bearing.

Description

technical field [0001] The invention belongs to the technical field of engine fault diagnosis, and in particular relates to an aeroengine bearing fault diagnosis method. Background technique [0002] General aviation activities are developing rapidly in our country. It has been widely used in flight operations in agriculture, forestry, fishery and construction, and is especially active in the fields of emergency rescue and disaster relief, meteorological monitoring, and scientific experiments. The reliability of general aviation aircraft has always been a priority in the aviation industry, because once a failure occurs, the consequences will be disastrous. For general aviation aircraft, predictive maintenance is of great significance in improving aircraft reliability and reducing operation and maintenance costs. The engine is the heart of an aircraft, and it is also the focus of aircraft predictive maintenance. Rolling bearings are one of the most vulnerable components of...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a fault diagnosis method for aero-engine bearings, which aims to solve the problem that in the current fault diagnosis of bearings, the state characteristics of a single mode commonly used are difficult to fully reflect the complex early bearing fault state information.

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