Compound fault diagnosis method of rolling bearings based on IDBO-TVFEMD and improved wavelet threshold function
BIE Fengfeng
ZHANG Yuting
LI Qianqian
DING Xueping
PENG Guangcheng
DAI Yuxuan
ZHANG Hanyang
Abstract：A fault diagnosis method based on improved dung beetle optimizer(IDBO)-time varying filtered empirical mode decomposition(TVFEMD)with improved wavelet threshold functions was proposed aiming at that the vibration signal of rolling bearing fault tends to be disturbed and overwhelmed by strong noise background.IDBO was primarily developed to iteratively optimize B-spline order and bandwidth threshold ξ in TVFEMD,and the optimal parameter combination was obtained.Applying TVFEMD on the original signal,the decomposition for intrinsic mode function(IMF)component series were achieved,among which the irrelevant components were removed by correlation coefficient criterion,and target signals were reconstructed.Then the improved wavelet threshold function was employed on the new signal for further denoising.Finally,the envelope spectrum of the processed signal was calculated,from which the typical fault characteristic frequency was extracted.Through simulation signal and fault simulation test analysis,the fault diagnosis method combined with IDBO-TVFEMD and improved wavelet threshold function was compared with empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD)and complete EEMD with adaptive noise(CEEMDAN)denoising methods.The research results show that the algorithm model proposed in this paper has higher efficiency.
Keywords：Rolling bearingTime varying filtered empirical mode decompositionDung beetle optimizer algorithmWavelet threshold function
Publication Date：2025-10-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：12( 51-62 )