Rolling element bearing fault detection using PPCA and spectral kurtosis

• A hybrid fault diagnosis method is developed to detect faults on rolling bearings.
• Probabilistic principal component analysis is applied to denose.
• Fast spectral kurtosis is employed to extract feature frequency.
• Numerical and experimental investigations are done to verify the performance.

A hybrid approach using probabilistic principal component analysis (PPCA) and spectral kurtosis (SK) is proposed to detect rolling element bearing faults. The approach includes three main steps. In a first step, the signal-to-noise ratio (SNR) of PPCA denoising model is improved through the selection of two key parameters. In the model, the primary information and fault signals will be preserved by allotted in the principal component subspace, while noises and linear interrelated information will be discarded by projected to the residual subspace. In a second step a band-pass filter for the denoising signal is designed using rapid spectral kurtosis procedure to determine optimal center frequency and bandwidth. The third step is to perform a Hilbert envelope spectrum analysis of the filtered signal to extract the fault frequencies of the rolling element bearings. The effectiveness of the proposed approach is demonstrated by numerical simulation and experimental investigation of rolling element bearing with different kind of faults. It indicates that employing the proposed scheme with PPCA and SK results in the effective detection of faults in rolling element bearings.