Compound fault diagnosis of rotating machinery based on adaptive maximum correlated kurtosis deconvolution and customized multiwavelet transform

Although compound fault diagnosis of rotating machinery based on vibration signals is a prominent method, it is still a challenge due to coupled fault features immersed in strong background noise. An adaptive deconvolution and denoising technology based on adaptive maximum correlated kurtosis deconvolution and multiwavelet transform is studied here. In combination with Hilbert envelope spectrum analysis, a novel compound fault diagnosis method is proposed in this paper. Based on analysis using maximum correlated kurtosis deconvolution (MCKD) theory, a parameter range estimation method is given which is favorable for MCKD parameter optimization. Flexible standard multiwavelets are used in post-processing of MCKD to enhance the denoising effect further. The minimum of a compound faults characteristic index composed of M-shift correlated kurtosis and square envelope spectrum entropy is adopted as the optimization criterion to set reasonable parameters for MCKD and construct customized multiwavelets using particle swarm optimization. The effectiveness of the proposed method is demonstrated by both simulated signal and practical vibration signals of a rotor test rig and an aero engine rotor experimental rig with different compound faults. The superior effectiveness and reliability of the proposed method are confirmed by comparison with other fault detection methods.