Fault diagnosis for rolling element bearing using variational mode decomposition and l 1 trend filtering

In order to extract the faint fault features of bearings in strong noise background, a method based on variational mode decomposition and l₁ trend filtering is proposed in this study. In the variational model, the mode number κ is determined difficulty, thus l₁ trend filtering is applied to simplify the frequency spectrum of the original signals. In this case, this parameter can be defined easily. At the same time, a criterion based on kurtosis is used to adaptively select the regularization parameter of l₁ trend filtering. The combined approach is evaluated by simulation analysis and the vibration signals of damaged bearings with a rolling element fault, an outer race fault and an inner race fault. The results demonstrate that the hybrid method is effective in detecting the three bearing faults. Moreover, compared with another approach based on multiscale morphology and empirical mode decomposition, the proposed method can extract more bearing fault features.