Research on TKEO fault diagnosis method based on SVD-morphological noise reduction

HUANG Gang-jin; FAN Yu-gang; FENG Zao; QI Peng

doi:10.13873/j.1000-9787(2017)07-0029-04

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Research on TKEO fault diagnosis method based on SVD-morphological noise reduction

更新时间：2025-09-11

- Research on TKEO fault diagnosis method based on SVD-morphological noise reduction
- Vol. 36, Issue 7, Pages: 29-32+37(2017)
- 作者机构：
  
  1. 昆明理工大学信息工程与自动化学院
  2. 云南省矿物管道输送工程技术研究中心
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)07-0029-04
  CLC： TH133.33
- Published：2017
- 稿件说明：
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[1]黄刚劲,范玉刚,冯早,齐鹏.基于SVD——形态降噪的TKEO故障诊断方法研究[J].传感器与微系统,2017,36(07):29-32+37.

HUANG Gang-jin, FAN Yu-gang, FENG Zao, et al. Research on TKEO fault diagnosis method based on SVD-morphological noise reduction[J]. 2017, 36(7): 29-32+37.
[1]黄刚劲,范玉刚,冯早,齐鹏.基于SVD——形态降噪的TKEO故障诊断方法研究[J].传感器与微系统,2017,36(07):29-32+37. DOI： 10.13873/j.1000-9787(2017)07-0029-04.

HUANG Gang-jin, FAN Yu-gang, FENG Zao, et al. Research on TKEO fault diagnosis method based on SVD-morphological noise reduction[J]. 2017, 36(7): 29-32+37. DOI： 10.13873/j.1000-9787(2017)07-0029-04.

摘要

针对强噪声干扰背景下微弱故障特征信息难以提取的问题

提出了一种基于奇异值分解（SVD）-形态降噪的Teager能量算子（TKEO）故障诊断方法。首先对轴承振动信号进行SVD

对得到的分量信号进行形态滤波

以滤除噪声干扰;然后利用峭度准则对分量信号进行筛选

并对其进行重构;最后利用TKEO计算重构信号的瞬时能量

得到信号的能量谱

提取振动信号的特征。将提出的方法应用于滚动轴承故障分析

结果表明该方法能清晰地提取故障特征信息。

Abstract

Aiming at problem of extracting weak characteristics from the fault signals containing strong background noise

a method based on singular value decomposition（ SVD）-morphological noise reduction for Teager-Kaiser energy operator（ TKEO） fault diagnosis is proposed. Firstly

bearing vibration signal is decomposed by SVD

the component signals are filtered by morphological filter to remove the noise; Secondly

the component signals are screened and reconstructed using kurtosis criterion; Finally

the instantaneous energy of reconstructed signals are calculated by using the TKEO

the energy spectrum of the signals are then obtained

from which the characteristics of the vibration signals are extracted. The experimental results show that the proposed method is capable of extracting fault features and has offered an approving performance on fault diagnosis of rolling bearing.

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references

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