Bearing fault diagnosis based on variational mode decomposition and total variation denoising

Feature extraction plays an essential role in bearing fault detection. However, the measured vibration signals are complex and non-stationary in nature, and meanwhile impulsive signatures of rolling bearing are usually immersed in stochastic noise. Hence, a novel hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation-minization based total variation denoising (TV-MM). The TV-MM approach is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the parameter lambda is very important in TV-MM, the weighted kurtosis index is also proposed in this work to determine an appropriate lambda used in TV-MM. The performance of the proposed hybrid approach is conducted through the analysis of the simulated and practical bearing vibration signals. Results demonstrate that the proposed approach has superior capability to detect roller bearing faults from vibration signals.