Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults

Abstract

A bearing's health state is closely linked to the reliable operation of rotating machinery. In this context, dynamic time warping (DTW) is an excellent fault classifier due to its outstanding distance measurement ability. However, DTW alone cannot obtain acceptable results when it is employed to handle signals with a certain degree of noise. An enhancement of DTW based on graph similarity guided symplectic geometry mode decomposition (GS-SGMD) is presented in this paper to improve the performance of traditional DTW. To reduce the interference of random noise in raw signals, the signal is decomposed into multiple components by SGMD. Then, graph similarity is introduced to select the effective component as a test sample to be detected. In addition, the templates of known fault states of bearings are also obtained by GS-SGMD. Finally, DTW is employed to recognize the fault type of the test sample. Experimental results show that the presented method can effectively detect bearing faults with higher precisions in comparison to DTW and SGMD.