改进DLMD和TKEO的滚动轴承故障特征提取方法

doi:10.12263/DZXB.20200423

电子学报 ›› 2021, Vol. 49 ›› Issue (2): 387-393.DOI: 10.12263/DZXB.20200423

改进DLMD和TKEO的滚动轴承故障特征提取方法

罗亭^1,2, 马军^1,2, 王晓东^1,2, 杨创艳^1,2, 李卓睿^1,2

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

收稿日期:2020-05-06 修回日期:2020-07-23 出版日期:2021-02-25
- 通讯作者:
- 马军
- 作者简介:
- 罗亭女,1995年12月出生于四川内江.现为昆明理工大学信息工程与自动化学院硕士研究生.主要研究方向为机械故障诊断及性能退化评估.E-mail:luoting@stu.kust.edu.cn
- 基金资助:
- 国家自然科学基金 (No.51765022，No.61663017）; 云南省科技计划项目 (No.2019FD042）

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

LUO Ting^1,2, MA Jun^1,2, WANG Xiao-dong^1,2, YANG Chuang-yan^1,2, LI Zhuo-rui^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China;
2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

Received:2020-05-06 Revised:2020-07-23 Online:2021-02-25 Published:2021-02-25
- Corresponding author:
- MA Jun
- Supported by:
- National Natural Science Foundation of China (No.51765022, No.61663017); Science and Technology Project of Yunnan Province (No.2019FD042)

摘要/Abstract

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.

关键词: 微分局部均值分解, 滚动轴承, 敏感因子, Teager能量算子

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.

Key words: differential local mean decomposition, rolling bearing, sensitive parameter, Teager-Kaiser energy operator

中图分类号:

TN911.7

罗亭, 马军, 王晓东, 等. 改进DLMD和TKEO的滚动轴承故障特征提取方法[J]. 电子学报, 2021, 49(2): 387-393.

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.

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改进DLMD和TKEO的滚动轴承故障特征提取方法

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

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[3]	苏维均, 杨飞, 于重重, 程晓卿, 崔世杰. 基于局部频谱的滚动轴承故障特征提取方法[J]. 电子学报, 2018, 46(1): 160-166.