Gearbox Rolling Bearing Fault Diagnosis Based on Autocorrelation Envelope and Adaptive MED
Li Hua
Yu Zifeng
Xiao Yuan
Li Zhiyong
Liu Haitao
Li Baisong
Shao Qiang
Gu Ziqiang
Abstract：Minimum entropy deconvolution(MED)is a popular algorithm in recent years,extensively ap-plied in feature extraction and fault diagnosis of components such as gearboxes and bearings.However,in the ac-tual computational process,the parameter settings of the MED inverse filter are highly sensitive to the extraction results.To address this issue,an optimized method was firstly proposed for extracting the fault characteristics of rolling bearings using MED.This method takes into account the energy proportion of feature frequencies under different lengths of inverse filters during the MED computation process,thereby determining the optimal parame-ters for the inverse filter.Additionally,the self-correlation of envelope signals is utilized to further enhance the weak fault characteristic signals of rolling bearings.By integrating self-correlated envelopes with the optimized MED method,a novel method for feature extraction and fault diagnosis of gearbox rolling bearings has been de-veloped.Simulations and tests have verified that this method can effectively enhance the characteristic signals related to bearing faults,and the optimized MED method is significantly superior to the traditional MED and oth-er related bearing signal processing methods.Notably,the self-correlated envelope,due to its ability to signifi-cantly enhance impulse components and its excellent denoising characteristics,shows more prominent results in the actual diagnosis of gearbox bearing faults.
Keywords：GearboxRolling bearingMinimum entropy deconvolutionAutocorrelated envelope
Publication Date：2024-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 139-148 )