A novel rolling bearing fault detect method based on feature mode decomposition and subtraction-average-based optimizer - Wei Xi; Fuyu Qiao; Jingjing Zhang (PLoS ONE, 2025)
Large rotating machinery is an essential piece of equipment in modern industry, playing a critical role in industrial production. However, the complex working environment complicates the extraction of fault-related information. This paper proposes a fault diagnosis method based on the subtraction-average-based optimizer (SABO) and feature mode decomposition (FMD). To address the issue that FMD’s decomposition performance is highly sensitive to its parameter settings, this paper uses the minimum envelope entropy as the fitness function and employs the SABO algorithm to adaptively optimize FMD’s two key parameters: the mode number (n) and filter length (L). Additionally, for the intrinsic mode functions (IMFs) obtained from FMD decomposition, the maximum kurtosis value is used to filter IMFs containing fault information, and envelope spectrum analysis is applied to achieve fault diagnosis. When applied to experimental signals of rolling bearing faults, the results demonstrate that the proposed method can extract the amplitude of the fault characteristic frequency from the envelope spectrum and accurately diagnose the fault type. Compared with methods based on empirical mode decomposition (EMD) and fixed-parameter FMD, the proposed method provides a more prominent representation of the fault characteristic frequency and its harmonics in the envelope spectrum. Furthermore, the proposed method achieves a more prominent representation of the fault eigenfrequency in the envelope spectrum and a lower error rate. The proposed method demonstrates significant potential and value for rolling bearing fault diagnosis.