最优解调频带识别及其在滚动轴承故障诊断中的应用

陈丙炎; 张卫华; 宋冬利; 程尧

doi:10.13890/j.issn.1000-128x.2019.05.120

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最优解调频带识别及其在滚动轴承故障诊断中的应用

试验检测 | 更新时间：2021-12-13

- 最优解调频带识别及其在滚动轴承故障诊断中的应用
- Identification of Optimal Demodulation Frequency Band and Its Application in Fault Diagnosis of Rolling Element Bearings
- 机车电传动 2019年第5期页码：137-143
- 作者机构：
  
  1.西南交通大学　牵引动力国家重点实验室，四川　成都　610031
- 作者简介：
  
  陈丙炎（1994—），男，硕士研究生，研究方向为机械设备状态监测。
- 基金信息：
  
  国家重点研发计划项目(2016YFB1200401-102A);牵引动力国家重点实验室自主课题(2018TPL-T01)
- DOI：10.13890/j.issn.1000-128x.2019.05.120
  中图分类号：
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陈丙炎, 张卫华, 宋冬利, 等. 最优解调频带识别及其在滚动轴承故障诊断中的应用[J]. 机车电传动, 2019,(5):137-143.

Bingyan CHEN, Weihua ZHANG, Dongli SONG, et al. Identification of Optimal Demodulation Frequency Band and Its Application in Fault Diagnosis of Rolling Element Bearings[J]. Electric Drive for Locomotives, 2019,(5):137-143.
陈丙炎, 张卫华, 宋冬利, 等. 最优解调频带识别及其在滚动轴承故障诊断中的应用[J]. 机车电传动, 2019,(5):137-143. DOI： 10.13890/j.issn.1000-128x.2019.05.120.

Bingyan CHEN, Weihua ZHANG, Dongli SONG, et al. Identification of Optimal Demodulation Frequency Band and Its Application in Fault Diagnosis of Rolling Element Bearings[J]. Electric Drive for Locomotives, 2019,(5):137-143. DOI： 10.13890/j.issn.1000-128x.2019.05.120.

摘要

针对在低信噪比和强非高斯噪声存在的情况下滚动轴承故障信号难以有效提取的问题，提出一种新的最优解调频带识别方法并应用于滚动轴承的故障诊断。该方法利用特定频带信号的包络谱幅值的稀疏度来度量故障脉冲，按照稀疏度最大原则自动识别最优解调频带；根据最优解调频带获得带通滤波后的最优解调信号，对最优解调信号的包络谱进行分析来识别滚动轴承故障及其类型。采用振动信号仿真模型和滚动轴承试验台获得的轴承故障信号来测试该方法的有效性，并把测试结果与快速峭度图方法进行对比。结果表明：该方法比快速峭度图方法能够更加准确地识别共振频带，并且在低信噪比和强非高斯噪声存在的情况下也能准确提取轴承故障特征。

Abstract

In order to effectively extract fault impulses of rolling element bearings in the presence of low signal to noise ratio and intense non-Gaussian noise, a new method for identifying optimal demodulation frequency band was presented and applied to fault diagnosis of rolling element bearings. The proposed method adopted the sparsity of frequency band signal to quantify fault impulses,and decomposed frequency band signal with maximal sparsity was selected as the optimal demodulation signal. Eventually, the bearing fault types can be identified from envelope spectrum of the optimal demodulation signal. To validate the effectiveness of the proposed method in bearing fault diagnosis, simulated signals and experimental signals of bearing localized faults were tested respectively and the performance of fast kurtogram was compared. The results indicated that the proposed method could more accurately recognize resonant frequency band than fast kurtogram and effectively extract bearing fault characteristics with the interference of low signal to noise ratio and intense non-Gaussian noise.

关键词

最优解调频带稀疏图滚动轴承故障诊断快速峭度图

Keywords

optimal demodulation frequency bandsparsegramrolling element bearingsfault diagnosisfast kurtogram

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