A new strategy of using a time-varying structure element for mathematical morphological filtering

This paper presents a novel signal processing scheme, namely time-varying morphological filtering (TMF), for rolling element bearing fault detection. In contrast with the multiscale morphological filtering (MMF) method, the structure element (SE) used in TMF is no longer fixed. It adjusts adaptively according to the extreme points of a signal so that the raw signal can be fit more accurately. In addition, the MMF needs to execute morphological operations multiple times, whereas the TMF can finish the filtering in one time operation. Consequently, TMF has a significant advantage in terms of computation efficiency. Experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of TMF. The results show that the proposed method can extract fault features of defective rolling element bearings with high computational efficiency. Moreover, five SEs are compared for TMF. The results show that the Dolph-Chebyshev SE performs best.