Study on compound fault diagnosis of rolling bearing based on dual-tree complex wavelet transform

Aiming at the difficulty of separating the fault feature from compound rolling bearing fault signal, a new fault diagnosis method is proposed based on dual-tree complex wavelet transform (DT-CWT) and independent component analysis (ICA). Firstly, DT-CWT is used to decompose the non-stationary fault vibration signal into several components with different frequency bands. Because frequency aliasing exists in the components, this problem disturbs the feature extraction of the fault signal. Then, ICA is introduced to perform blind source separation on the mixed signal consisting of various components to eliminate the frequency aliasing as far as possible. Finally, Hilbert envelope decomposition is performed on the independent signal components separated from the mixed signal. Thus the compound fault feature information can be separated, and the fault identification is achieved. The experiment results show that the proposed method can effectively separate and extract the feature information of the compound rolling bearing faults, which verifies the feasibility and effectiveness of the proposed method.