Multi-sensor fusion convolutional neural network aero-engine bearing fault diagnosis method

Abstract

The invention relates to a multi-sensor fusion convolutional neural network aero-engine bearing fault diagnosis method. The method comprises the steps of S1, performing data acquisition; s2, performing data preprocessing; s3, taking the data acquired by the analogue simulation test platform as source domain data, and taking the data acquired by the online monitoring system as target domain data; s4, building a multi-sensor information fusion 1D-CNN model, and putting the source domain data into the source domain 1D-CNN model for training; s5, carrying out target domain bearing online diagnosis; s6, generating a fault diagnosis result. Vibration signals of different positions of aero-engine bearing in different fault states are collected, multi-channel input 1D-CNN model is adopted, data collected by vibration acceleration sensors at different positions are fused and put into model for training, and target domain bearing online diagnosis is carried out. Therefore, the fault diagnosis and analysis are carried out on the bearing of the rotating mechanical part of the aero-engine, so that fault type identification is accurately completed, and the process of manual feature mining in a traditional method is omitted.

Description

technical field [0001] The invention relates to the field of fault diagnosis of electromechanical systems, in particular to a multi-sensor fusion convolutional neural network aeroengine bearing fault diagnosis method. Background technique [0002] According to the statistical report on my country's civil aviation safety information in 2018, nearly 40% of the general aviation accidents that occur each year are caused by mechanical problems such as equipment system failures, failures, wear and tear of key parts, and the engine as an aviation aircraft. The key power components, due to their complex mechanical structure, high temperature and high pressure and other harsh operating environments, are more prone to mechanical damage to their internal components after a period of use. The failures of aeroengines are mostly caused by the shafting parts (such as gears, bearings, etc.) that constitute its rotating mechanism. Once the surface of the parts fails, breaks, etc., it is easy ...

AI Technical Summary

Problems solved by technology

[0003] Aeroengine mechanical failures can generally be divided into gas path failures, accessory failures, and rotating machinery failures. Rotating machinery failures are analyzed and solved based on traditional physical mechanism dynamic models due to their various types of failures and unobvious failure characteristics. It is difficult to accurately identify, and with the rapid development of information technology in the 21st century and the application of artificial intelligence technology, aeroengine fault diagnosis is also moving towards intelligence and automation.

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