A novel weak fault signal detection approach for a rolling bearing using variational mode decomposition and phase space parallel factor analysis

Abstract

Considering the difficulty of fault feature extraction from a rolling bearing under strong background noise, we present a novel approach based on variational mode decomposition (VMD) and phase space parallel factor analysis, for detecting the weak fault signal of a rolling bearing. In this scheme, the VMD method is first adopted to decompose the raw vibration signal into several intrinsic mode components. The selected intrinsic mode function component with maximal kurtosis is subsequently embedded into the high dimensional phase space by phase space reconstruction. Then, the independent components are estimated in the high dimensional phase space by parallel factor analysis. In addition, a new criterion combining kurtosis and feature energy factor (FEF) is proposed for the selection of the time delay and embedding dimension. Finally, the optimal independent component with the largest FEF is selected for envelope spectrum analysis, and the faint fault characteristic frequencies of the vibration signal can be extracted. The feasibility of the proposed scheme is demonstrated through simulation and experimental vibration signals. Results indicate that the proposed method has better capability in detecting a weak fault signal of a rolling bearing, compared with VMD-fast spectral kurtogram and phase space independent component analysis.