Wavelet Neural Network Aided On-line Detection and Diagnosis of Rotating Machine Fault

An effective approach for multi-concurrent fault diagnosis of aeroengine based on integration of fractal exponent wavelet analysis and neural networks is presented. The wavelet transform can accurately localizes the characteristics of a signal both in the time and frequency domains and in a view of the inter relationship of wavelet transform between fractal theory, the whole and local fractal exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function (RBF) for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improved Levenberg-Marquardt (LM) optimization technique is used to fulfill the network structure and parameter identification. By means of choosing enough samples to train the fault diagnosis network and the information representing the faults is input into the trained wavelet network, and according to the output result the type of fault can be determined. The robustness of exponent wavelet network for fault diagnosis is discussed. The practical multi-concurrent fault diagnosis for aeroengine vibration approves to be accurate and comprehensive. The method can be generalized to other devices' fault diagnosis.