基于RLMD和Kmeans++的轴承故障诊断方法
Bearing Fault Diagnosis Method based on RLMD and Kmeans++
- 机械传动 2021年45卷第2期 页码：163-170
- 作者机构：
1.青岛理工大学 信息与控制工程学院， 山东 青岛 266500
2.郑州机械研究所有限公司， 河南 郑州 450001
- 作者简介：
颜少廷（1994— ），男，河南新郑人，在读硕士研究生，研究方向为机械故障诊断。
- 基金信息：青岛理工大学新工科研究与实践项目(F2018-119)
DOI：10.16578/j.issn.1004.2539.2021.02.025
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颜少廷,周玉国,任艳波等.基于RLMD和Kmeans++的轴承故障诊断方法[J].机械传动,2021,45(02):163-170.
Yan Shaoting,Zhou Yuguo,Ren Yanbo,et al.Bearing Fault Diagnosis Method based on RLMD and Kmeans++[J].Journal of Mechanical Transmission,2021,45(02):163-170.
颜少廷,周玉国,任艳波等.基于RLMD和Kmeans++的轴承故障诊断方法[J].机械传动,2021,45(02):163-170. DOI： 10.16578/j.issn.1004.2539.2021.02.025.
Yan Shaoting,Zhou Yuguo,Ren Yanbo,et al.Bearing Fault Diagnosis Method based on RLMD and Kmeans++[J].Journal of Mechanical Transmission,2021,45(02):163-170. DOI： 10.16578/j.issn.1004.2539.2021.02.025.
摘要
为了提升轴承故障诊断性能，提出了一种基于鲁棒局部均值分解（RLMD）和Kmeans++的轴承故障诊断方法。利用RLMD方法对轴承振动信号进行分解，得到乘积函数（PF），根据PF分量与原始振动信号的相关程度选择敏感PF分量，叠加敏感PF分量构成重构信号；通过计算原始振动信号和重构信号的时域、频域统计特征形成轴承故障特征集；利用线性判别分析（LDA）提取轴承故障的Fisher特征；通过Kmeans++聚类的方法对故障特征进行聚类，得到各工况轴承的聚类中心；通过计算测试样本与聚类中心之间的汉明贴近度来实现轴承故障诊断。利用含有不同信噪比的仿真轴承故障数据和Paderborn大学轴承数据中心的轴承故障数据评价所提出方法的有效性。结果表明，该方法即使在样本数较少的情况下也能够准确地识别出不同类别和级别的轴承故障。
Abstract
To improve the performance of bearing fault diagnosis， a bearing fault diagnosis method based on Robust Local Mean Decomposition （RLMD） and Kmeans++ is proposed. The product functions （PF） are obtained by decomposing the bearing vibration signal using the RLMD technique. The sensitive PF components are sifted by calculating the correlation coefficients between the PF components and the original vibration signal， and the sensitive PF components are superimposed to form the reconstructed signal. The bearing fault feature set is formed by calculating the time and frequency domain statistical features of the original vibration signal and the reconstructed signal. The Fisher features of bearing failure feature are extracted by linear discriminant analysis （LDA）. The fault feature is clustered by the Kmeans++ clustering method and the cluster center of each bearing working condition is got. The bearing fault identification is realized by calculating the Hamming approach degree between the test sample and the cluster center. The simulated bearing data with different signal-to-noise ratios and bearing data from the Paderborn university test bench are used to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify bearing faults with different categories and levels even though the number of training sample is small.
关键词
轴承故障诊断鲁棒局部均值分解线性判别分析Kmeans++汉明贴近度
Keywords
BearingFault diagnosisRobust local mean decomposition（RLMD）Linear discriminant analysis（LDA）Kmeans++Hamming approach degree
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