一种新的图谱域滚动轴承早期故障检测与识别方法

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摘要健康状态在线诊断是保证滚动轴承可靠运行的重要手段。局部均值分解作为一种自适应分解方法，其分解得到的分量信号可对非平稳信号进行多尺度描述，但其分解得到的分量信号往往存在数据量过大，难以直接从中获取早期微弱故障特征等问题。为了解决上述问题，本文提出了一种新的图谱域滚动轴承早期故障检测与识别方法。首先利用局部均值分解，将滚动轴承振动信号分解到多个尺度，在此基础上采用图论方法对振动信号进行动态建模；计算相邻模型间的相似性建立起动态特性的量化指标，依据拉依达准则对早期故障进行检测决策；最后采用模式分类方法实现故障类型的识别。本文方法分别在XJTU-SY和CWRU数据集上进行了实验验证，结果表明该方法能够有效实现滚动轴承的早期故障检测与识别的任务。

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	陈子旭1
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	卢国梁1
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关键词 ：滚动轴承, 早期故障检测, 故障诊断, 图建模, 局部均值分解

Abstract：Health status online diagnosis is an important way to ensure the reliable operation of rolling element bearings(REB). As a self-adaptive decomposition method, local mean decomposition(LMD) can describe the non-stationary signal into multi-scale. However, the decomposed component signal always has a large scale and is hard to extract weak fault features. To solve these problems, a novel early fault detection and diagnosis method for rolling element bearings in graph spectrum domain is proposed. We first decompose the vibration signal into multi-scale by LMD. Based on the generated component signal, the graph theory is used for dynamically modeling of rolling element bearings. Then a quantitative index of dynamic characteristics can be established by calculating the similarity between adjacent models, pauta criterion is employed to make early fault detection. Finally, the pattern recognition method is used to make fault diagnosis. The experiment on XJTU-SY and Case Western Reserve University(CWRU) data sets demonstrate the effectiveness of our method.

Key words： rolling element bearings early fault detection fault diagnosis graph modeling local mean decomposition

收稿日期: 2020-10-14 出版日期: 2022-03-28

引用本文:

陈子旭1,2,3，朱振杰1,2,3，卢国梁1,2,3. 一种新的图谱域滚动轴承早期故障检测与识别方法[J]. 振动与冲击, 2022, 41(6): 51-59.
CHEN Zixu1,2,3，ZHU Zhenjie1,2,3，LU Guoliang1,2,3. Novel early fault detection and diagnosis for rolling element bearings in graph spectrum domain. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 51-59.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I6/51

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