AutoVMDPgram: An Effective Method for Fault Diagnosis of Rolling Bearing

In previous studies, the VMDPgram was creatively proposed by combining variational mode decomposition (VMD) with wavelet packet transform (WPT). Although the VMDPgram demonstrates excellent performance in bearing fault diagnosis, there are still some issues that need to be further studied. In light of this, this work conducts the in-depth studies of VMDPgram for the unresolved issues. First, in view of the obvious second-order cyclostationarity of vibration signal of rotating machinery such as bearing, especially in the presence of localized faults, the unbiased autocorrelation (AC) function is introduced. Here, the kurtosis value of the unbiased AC of the squared envelope of each sub-intrinsic modal function (sub-IMF) within the constrained range is calculated, generating the new method named AutoVMDPgram. Second, the modified adaptive resonance bandwidth (MARB) is introduced to constrain the decomposition depth of the AutoVMDPgram. Third, the cumulative evaluation index based on the unbiased AC kurtosis of the square envelope of the sub-IMF is proposed as a measure to locate the optimal sub-IMF without determining whether the resonant frequency range is divided into different sub-IMFs. AutoVMDPgram is tested on simulated and experimental data and compared with Autogram, spectral kurtosis (SKs), and VMD to evaluate its performance in rolling bearing diagnostics.