A fault information-oriented weighted nuclear norm minimization method and its application to fault feature extraction in a rolling bearing

How to effectively extract fault features from noisy vibration signals is a key problem to be solved in bearing fault diagnosis. The difficulty here mainly lies in the fact that the components of interest are almost submerged by background noise and external interference. To accurately implement extraction of fault features, nuclear norm minimization (NNM) has been introduced to fault feature extraction due to its powerful denoising capability. However, NNM treats all singular values equally, which may result in components of interest with small singular values being abandoned, especially when a bearing defect is at its incipient stage. Therefore, to enhance the flexibility and effectiveness of NNM in handling practical problems, fault information-oriented weighted nuclear norm minimization (FIWNNM) is proposed for extracting bearing fault features, in which singular values are treated differently by assigning appropriate weights according to the physical meanings. Through the proposed FIWNNM, fault features can be drawn from a noisy signal, even when contaminated by strong noise and external interferences. The analysis results from both simulated and real measured signals are used as the verification of the proposed FIWNNM.