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- W2052616376 abstract "An effective approach for fault diagnosis of aeroengine based on integration of wavelet analysis and neural networks ispresented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains andin a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtainedfrom wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radialbasis function for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improvedLevenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. Bychoosing enough samples to train the fault diagnosis network and the information representing the faults input into theneural network, the fault pattern can be determined. The robustness of wavelet neural network for fault diagnosis isdiscussed. The practical fault diagnosis for aeroengine vibration approves to be accurate and comprehensive." @default.
- W2052616376 created "2016-06-24" @default.
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- W2052616376 date "2008-10-10" @default.
- W2052616376 modified "2023-09-23" @default.
- W2052616376 title "Performance measurement of fault pattern classification for aircraft engine based on wavelet network" @default.
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- W2052616376 doi "https://doi.org/10.1117/12.806865" @default.
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