Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing

LUO Ting; MA Jun; WANG Xiao-dong; YANG Chuang-yan; LI Zhuo-rui

doi:10.12263/DZXB.20200423

您当前的位置：

首页 >

文章列表页 >

Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing

更新时间：2025-12-08

- Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing
- Acta Electronica Sinica Vol. 49, Issue 2, Pages: 387-393(2021)
- 作者机构：
  
  1. 昆明理工大学信息工程与自动化学院,云南,昆明,650500
  2. 昆明理工大学云南省人工智能重点实验室,云南,昆明,650500
  3. 昆明理工大学信息工程与自动化学院,云南,昆明,650500
  4. 昆明理工大学云南省人工智能重点实验室,云南,昆明,650500
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51765022, No.61663017);Science and Technology Project of Yunnan Province (No.2019FD042)
- DOI：10.12263/DZXB.20200423
  CLC： TN911.7
- Published Online：25 February 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
LUO Ting, MA Jun, WANG Xiao-dong, et al. Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing[J]. Acta Electronica Sinica, 2021, 49(2): 387-393.
DOI：

LUO Ting, MA Jun, WANG Xiao-dong, et al. Improved DLMD and TKEO Method for Fault Feature Extraction of Rolling Bearing[J]. Acta Electronica Sinica, 2021, 49(2): 387-393. DOI： 10.12263/DZXB.20200423.

摘要

针对微分局部均值分解（Differential Local Mean Decomposition，DLMD）不能自适应判断微分次数的问题，提出一种改进DLMD和Teager能量算子（Teager-Kaiser Energy Operator，TKEO）解调的滚动轴承故障特征提取方法.首先，构建中点-局部均值距离与绝对偏度之和的DLMD微分次数判定指标，将信号分解为若干个乘积函数（Product Function，PF）分量；其次，计算敏感因子筛选有效PF分量并重构；最后，计算TKEO谱，提取滚动轴承的故障特征.实验对比分析表明，所提方法能自适应判断DLMD的微分次数，并有效提取滚动轴承故障特征.

Abstract

In order to solve the problem that differential local mean decomposition (DLMD) can’t adaptively determine the differential degree

a rolling bearing fault feature extraction method based on improved DLMD and Teager-Kaiser energy operator (TKEO) demodulation is proposed. Firstly

the index of DLMD differential degree based on the sum of midpoint local mean distance and absolute skewness is constructed

and the signal is decomposed into several product function (PF) components; Secondly

the sensitive factors are calculated

and the effective PF components were screened and reconstructed; Finally

the TKEO spectrum is calculated to extract the fault features of the rolling bearing. The experimental results show that the proposed method can adaptively judge the differential degree of DLMD and effectively extract the fault features of rolling bearing.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings

Enhanced Empirical Wavelet Transform Based Time-Frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis

Rolling Bearing Fault Feature Extraction Method Based on Local Spectrum

Related Author

YANG Chuang-yan

MA Jun

WANG Xiao-dong

LUO Ting

LI Zhuo-rui

ZHENG Jin-de

PAN Hai-yang

QI Xiao-li

Related Institution

School of Mechanical Engineering, Anhui University of Technology, Maanshan

Department of Computer and Information Engineering, Beijing Technology and Business University

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University

Department of Computer and Information Engineering Beijing Technology and Business University Beijing China

State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

⁰