[1]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,(05):1.[doi:10.13890/j.issn.1000-128x.2019.05.120]
 CHEN Bingyan,ZHANG Weihua,SONG Dongli,et al. Identification of Optimal Demodulation Frequency Band and Its Application in Fault Diagnosis of Rolling Element Bearings[J].Electric Drive for Locomotives,2019,(05):1.[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()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2019年05期
页码:
1
栏目:
试 验 检 测
出版日期:
2019-09-10

文章信息/Info

Title:
 Identification of Optimal Demodulation Frequency Band and Its Application in Fault Diagnosis of Rolling Element Bearings
作者:
 陈丙炎张卫华宋冬利程 尧
 (西南交通大学 牵引动力国家重点实验室,四川 成都 610031)
Author(s):
 CHEN Bingyan ZHANG Weihua SONG Dongli CHENG Yao
 ( State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, China )
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 band sparsegram rolling element bearings fault diagnosis fast kurtogram
分类号:
TH133.33;U260.331+.2
DOI:
10.13890/j.issn.1000-128x.2019.05.120
文献标志码:
A
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.

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备注/Memo

备注/Memo:
 收稿日期:2018-10-16
基金项目: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)
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。
更新日期/Last Update: 2019-09-09