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Research on Compound Faults Diagnosis of Rolling Bearings Based on Parallel Dual Q-Factors and AMCKD

The rolling bearings often suffer from compound faults in practice, moreover, several faults add together and interfere with each other, which make it difficult to separate week fault signals from them through conventional ways. In order to improve the compound faults diagnosis of rolling bearings via signals' separation, the paper proposes a new method to identify compound faults from mixed-signals, which is based on parallel dual Q-factors method and the adaptive maximum correlation kurtosis deconvolution (AMCKD) method. With the approach, the vibration signal is firstly decomposed into high and low resonance components by the sparse decomposition based on dual Q-factors method, which can sparsely represent the signal and extract the fault impact signal. Then, the low-resonance component is processed by AMCKD, and the AMCKD can adaptively optimize the selection of parameters M and L. Finally, the compound faults can be separated effectively by the method, which makes the fault features more easily extracted and more clearly identified. Simulated analysis result validates the effectiveness of the proposed method in compound faults separating.