Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

By introducing the Ramanujan subspace and proposing the adaptive periodic mode decomposition (APMD) method, this study effectively identifies and extracts periodic components (PCs) in rolling bearing signals, providing a valid approach for rolling bearing fault diagnosis.