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13 October 2008 Performance measurement of fault pattern classification for aircraft engine based on wavelet network
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Abstract
An effective approach for fault diagnosis of aeroengine based on integration of wavelet analysis and neural networks is presented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains and in a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improved Levenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. By choosing enough samples to train the fault diagnosis network and the information representing the faults input into the neural network, the fault pattern can be determined. The robustness of wavelet neural network for fault diagnosis is discussed. The practical fault diagnosis for aeroengine vibration approves to be accurate and comprehensive.
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Wei Liao and Pu Han "Performance measurement of fault pattern classification for aircraft engine based on wavelet network", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 712828 (13 October 2008); doi: 10.1117/12.806865; https://doi.org/10.1117/12.806865
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