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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Chen Ming
Chen Ming
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ASME Press
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Aero-engine rotor fault endangering safety of aircraft, which needed to be real-time monitored. Flight Data Recorder (FDR) recorded engine parameters can objectively reflect on the engine working conditions, so the analysis of engine parameters and the in-depth study of flight data are significant to effective implementation of aircraft maintenance and condition monitoring. The article mainly uses aircraft engine rotor vibration signals recorded by the FDR to put forward an aero-engine rotor fault diagnosis method, which is based on wavelet analysis and neural network. The method uses wavelet time-frequency analyzes technique to filter the noise and waves of the aero-engine rotor fault vibration signals. Use wavelet packet to decompose quotient and to gain the frequency band energy. To summarize the fault characteristics according to the frequency energy changes, and use BP neural network for fault identification, finally to realize it by using Matlab simulation software. The results show that the method does not require the establishment of aero-engine fault diagnose model, and can effectively enhance the accuracy of aero-engine rotor fault diagnosis.

The Basic Theory of Wavelet Neural Network
The Diagnosis of Engine Vibration Signal
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