Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis. (December 2021) - Cheng, Jian; Yang, Yu; Li, Xin; Cheng, Junsheng
Highlights: A novel method of period extraction, called APMD, is proposed for rolling bearing fault diagnosis. APMD can accurately extract the periodic impulse components by projecting vibration signal into Ramanujan subspaces corresponding to different exact periods. APMD can realize the adaptive signal decomposition without setting parameters. The experiment results show that the proposed method is a valid fault diagnosis method of rolling bearing. Abstract: As is known to all, rolling bearing fault will induce periodic impulses. Although the existing fault diagnosis methods, such as wavelet transform (WT) and ensemble empirical mode decomposition (EEMD), can effectively diagnose rolling bearing fault, their weak period recognition abilities limit their application in rolling bearing fault diagnosis. The Ramanujan subspace is introduced from the perspective of period recognition and extraction, and an adaptive periodic mode decomposition (APMD) method that can extract periodic components (PCs) without setting any parameters is proposed in this paper. APMD determines the major periods (MPs) in the signal by periodicity measurement, and projects the signal into respective Ramanujan subspaces of MPs to form the corresponding PCs. The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis.