A NOVEL FAULT DIAGNOSIS METHOD FOR ROTATING MACHINERY BASED ON EEMD AND MCKD

Incipient fault diagnosis of rotating machinery has received extensive research attention for years. However, the diagnosis remains a difficult problem since the incipient faults are generally quite weak in noisy environments. In the present work, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and maximum correlated kurtosis deconvolution (MCKD) is proposed in order to solve this problem. For the incipient fault of rotating machines, EEMD is firstly performed and the signal is reconstructed based on the calculation correlation coefficient and kurtosis. The impulsive components of faults can be enhanced using the MCKD-based adaptive method, and the weak fault features can be extracted from the envelope spectrum. Finally, the diagnosis results are output. The experimental results indicate that, using the proposed method, the fault impulsive components in the obtained intrinsic mode functions (IMFs) with EEMD can be adaptively enhanced, and the weak fault signal hidden in the noise can be effectively detected.