基于拉普拉斯特征映射的滚动轴承故障识别

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (5) : 128-134.

论文

基于拉普拉斯特征映射的滚动轴承故障识别

用传统的线性方法对非平稳和非线性运行状态的滚动轴承进行故障诊断时，效果欠佳。为了及时、准确地监测轴承的运行状态，提出了将拉普拉斯特征映射算法(Laplacian Eigenmap LE)应用到滚动轴承的故障识别中。在振动信号构建的时域和频域高维特征空间矩阵中，充分利用LE算法在非线性特征提取和降维的优点，进行学习，提取表征轴承状态的特征量，并以可视化的聚类结果进行表示。实验模拟了轴承的4种不同类型故障以及滚动体的4种不同受损程度，采用模式识别中聚类性的类内距和类间距两个参数作为衡量指标。与PCA和KPCA两种方法对比，LE不仅明显识别出四种故障类型和有效的区分出滚动体的不同受损程度，而且识别率大大提高。并通过测试样本组验证了LE方法的有效性。

作者信息 +

A Method for Fault Diagnosis of Rolling Bearings Based on Laplacian Eigenmap

The traditional linear diagnosis methods for rolling bearing fault with non-stationary and nonlinear running status was not effective. In order to monitor the rolling status accurately and timely, a new diagnosis method was put forward by applying the algorithm of Laplacian Eigenmap (LE) to the diagnosis of rolling. It fully used the advantage of LE algorithm for extracting nonlinear features and reducing dimension for characteristic space matrix in the time domain and frequency domain constructed by vibration signal，extracted the features of running status of external rolling and visualized the clustering results. The experiments using two parameters (between-class scatter and within-distance in pattern recognition) as the measurable indicators simulated four different faults of the bearings and the four different extent of the damage of balls in bearing. Compared with PCA & KPCA, LE clearly identifies the four different faults and the different extent of the damage of balls, and its recognition rate rises greatly. The effectiveness of LE has been Verified by testing samples.

Author information +

文章历史 +

摘要

用传统的线性方法对非平稳和非线性运行状态的滚动轴承进行故障诊断时，效果欠佳。为了及时、准确地监测轴承的运行状态，提出了将拉普拉斯特征映射算法(Laplacian Eigenmap LE)应用到滚动轴承的故障识别中。在振动信号构建的时域和频域高维特征空间矩阵中，充分利用LE算法在非线性特征提取和降维的优点，进行学习，提取表征轴承状态的特征量，并以可视化的聚类结果进行表示。实验模拟了轴承的4种不同类型故障以及滚动体的4种不同受损程度，采用模式识别中聚类性的类内距和类间距两个参数作为衡量指标。与PCA和KPCA两种方法对比，LE不仅明显识别出四种故障类型和有效的区分出滚动体的不同受损程度，而且识别率大大提高。并通过测试样本组验证了LE方法的有效性。

Abstract

The traditional linear diagnosis methods for rolling bearing fault with non-stationary and nonlinear running status was not effective. In order to monitor the rolling status accurately and timely, a new diagnosis method was put forward by applying the algorithm of Laplacian Eigenmap (LE) to the diagnosis of rolling. It fully used the advantage of LE algorithm for extracting nonlinear features and reducing dimension for characteristic space matrix in the time domain and frequency domain constructed by vibration signal，extracted the features of running status of external rolling and visualized the clustering results. The experiments using two parameters (between-class scatter and within-distance in pattern recognition) as the measurable indicators simulated four different faults of the bearings and the four different extent of the damage of balls in bearing. Compared with PCA & KPCA, LE clearly identifies the four different faults and the different extent of the damage of balls, and its recognition rate rises greatly. The effectiveness of LE has been Verified by testing samples.

导出引用

黄宏臣，韩振南，张倩倩，李月仙，张志伟. 基于拉普拉斯特征映射的滚动轴承故障识别[J]. 振动与冲击, 2015, 34(5): 128-134

HUANG Hong-chen,HAN Zhen-nan,Zhang Qian-qian，LI Yue-Xian, ZHANG Zhi-wei. A Method for Fault Diagnosis of Rolling Bearings Based on Laplacian Eigenmap[J]. Journal of Vibration and Shock, 2015, 34(5): 128-134

参考文献

[1] 钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,2006:298-299;171-180.
Zhong Bing-lin，Huang Ren. Introduction to Machine Fault Diagnosis[M].Beijing: China Machine Press, 2006: 171-180; 298-299.
[2] Jong-Min Lee, Chang Kyoo Yoo. Nonlinear process monitoring using kernel principal component analysis [J], Chemical Engineering Science 59 (2004) 223-234.
[3] 邓晓刚,田学民.一种基于KPCA 的非线性故障诊断方法[J].山东大学学报,2005，Vol.35, No.3.
    Deng Xiao-gang, Tian Xue-min，Nonlinear process fault diagnosis method using kernel principal component analysis, Journal of Shandong University, 2005, Vol.35, No.3.
[4] 蒋玲莉.基于核方法的旋转机械故障诊断技术与模式分析方法研究[D].长沙:中南大学,2010.
    Jiang Ling-li, Fault Diagnosis and Pattern Analysis for Rotating Machinery Based on Kernel Method[D], Changsha:Central South Universtiy, 2010.
[5] Seung H S, Daniel D L. The manifold ways of perception [J]. Science, 2000, 290 (5500): 2268-2269.
[6] Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500) : 2323-2326.
[7] Tenenbaum J, Silva D D, Langford J. A global geometric framework for nonlinear dimensionality reduction [J]. Science,2000,290 (5500) : 2319-2323.
[8] 王泽杰,胡浩民.流形学习算法中的参数选择问题研究[J].计算机应用与软件,2010,Vol.27,No.6.
    Wang Ze-jie, Hu Hao-min. On Parameter Selection in Manifold Learning Algorithm [J]. Computer Applications and Software, 2010,Vol.27 No. 6.
[9] 何清波.多元统计分析在设备状态监测诊断中的应用研究[D].合肥:中国科学技术大学,2007.
    Qingbo He, Research on Multivariate Statistical Analysis for Machine Condition Monitoring and Diagnosis [D].Hefei: University of Science and Technology of China, 2007.
[10] 贾茂林,王孙安,梁霖.利用非线性流形学习的轴承早期故障特征提取方法[J].西安交通大学学报,2010,Vol.44, No.5.
    Jia Mao-lin，Wang Sun-an，Liang Lin. Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning[J].Journal of Xi’an Jiaotong University, 2010, Vol.44, No.5.
[11] 盛兆顺，尹琦岭.设备状态监测与故障诊断技术及应用[M]. 北京:化学工业出版社, 1991.40-41.
    Sheng Zhao-shun, Yin Qi-ling. Equipment Condition Monitoring and Fault Diagnosis Technology and Application [M].Beijing: Chemical Industry Press,1991.40-41.
[12] The Case Western Reserve University Bearing Data Center.
http://csegroups.case.edu/bearingdatacenter/home.
[13] 张学工.模式识别[M].北京:清华大学出版社, 2010.146.
    Zhang Xue-gong，Pattern Recognition[M]. Beijing: Tsinghua University press, 2010.146.
[14] Qingbo He. Vibration signal classification by wavelet packet energy flow manifold learning [J]. Journal of Sound and Vibration 332 (2013) 1881-1894.
[15] Qingbo He. Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis. Mechanical Systems and Signal Processing 35 (2013) 200-218
[16] 蒋全胜，李华荣，黄鹏. 一种基于非线性流形学习的故障特征提取模型[J]. 振动与冲击，2012，Vol.31 No.23.
Jiang Quan-sheng，Li Hua-rong，Huang Peng. A fault feature extraction model based on nonlinear manifold learning [J] . Journal of Vibration and Shock，2012，Vol.31 No.23.