A Sparse Fault Feature Extraction Method for Rotating Machinery Based on Q Factor Wavelet Multi-resolution Decomposition

In order to enhance the adaptive ability of Q factor wavelet and realize the multi-resolution decomposition of signal in the analysis filter bank, a sparse feature extraction method based on the multi-resolution decomposition of Q factor wavelet is proposed. In this method, the multi-order binary analysis filter banks are firstly constructed by using the Q factor wavelet, and then the optimal sub-band is selected by optimizing the iterative Q factor. Then, the shock interval of the optimal sub-band is selected as the atom, and the atom forms a complete dictionary through toeplitz extension to realize the sparse decomposition of the signal. Finally, the sparse signal is analyzed by envelope demodulation, and the fault characteristic frequency can be extracted effectively, which proves that the sparse signal has the ability to express fault features. The simulation and experimental results show that this method can effectively extract sparse feature of signals compared with DCT and DHT dictionaries. It not only overcomes the weakness of adaptive ability of traditional complete dictionaries, but also can effectively express sparsely.