基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (10) : 65-74.

论文

黄海松，魏建安，任竹鹏，吴江进

作者信息 +

Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM

HUANG Haisong，WEI Jian’an，REN Zhupeng，WU Jiangjin

Author information +

文章历史 +

摘要

针对传统支持向量机(SVM)算法在滚动轴承故障诊断领域中，对失衡数据集效果不佳、对噪声敏感以及对本身参数依赖较大等缺点，提出一种基于样本特性的过采样算法(OABSC)。该算法利用改进凝聚层次聚类将故障样本分成多个簇；在每个簇中综合考虑样本距离、近邻域密度对“疑似噪声点”进行识别、剔除，并将剩余样本按信息量进行排序；紧接着，在每个簇中采用K-信息量近邻域(KINN)过采样算法合成新样本，以使得数据集平衡；模拟3种不同失衡比下的轴承故障情况，并采用粒子群算法优化了SVM分类器的参数。经试验证明：相比已有算法，OABSC算法能更好地适用于数据呈多簇分布且失衡的轴承故障诊断领域，拥有更高的G-mean值与AUC值以及更强的算法鲁棒性。

Abstract

Aiming at the shortcomings of the standard support vector machine (SVM) in the field of rolling bearing fault diagnosis, such as poor performance on imbalanced datasets, sensitivity to noise, and heavy dependence on its own parameters, an oversampling algorithm based on sample characteristics (OABSC) was proposed.First,improved agglomeration hierarchical clustering was used to divide the failure samples into multiple clusters.Then, the sample distance and the neighborhood density in each cluster were comprehensively considered to identify and remove “suspected noisy points”, and sort the remaining samples according to the amount of information.Further, K -information nearest neighbors (K INN) oversampling algorithm in each cluster was utilized to synthesize new samples to balance the dataset.Finally, bearing failures at three different imbalance ratios were simulated and the parameters of the SVM classifiers were optimized by using particle swarm optimization (PSO).The experiments show that, compared with the existing algorithms, the proposed OABSC algorithm is better applicable to the field of bearing fault diagnosis where the data is distributed in multiple clusters and is imbalanced.It has higher G-mean value and AUC value, and stronger algorithm robustness.

导出引用

黄海松，魏建安，任竹鹏，吴江进. 基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(10): 65-74

HUANG Haisong，WEI Jian’an，REN Zhupeng，WU Jiangjin. Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM[J]. Journal of Vibration and Shock, 2020, 39(10): 65-74

参考文献

[1] 刘永强, 李翠省, 廖英英. 基于 EEMD 和自相关函数峰态系数的轴承故障诊断方法[J]. 振动与冲击, 2017, 36 (2):111-116.

[2] Liu Yongqiang,Li Cuixing,Liao Yingying . Fault diagnosis method for rolling bearings based on EEMD and autocorrelation function kurtosis[J].Journal of Vibration and Shock , 2017, 36(2):111-116.

[3] 陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断[J]. 仪器仪表学报, 2017, 38(1):33-40.

Chen Chao,Shen Fei,Yan Ru qiang.Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis[J].Chinese Journal of Scientific Instrument, 2017,38(1):33-40.

[4] Duan L,Xie M, Bai T, et al. A new support vector data descrip-tion method for machinery fault diagnosis with unbalanced datasets[J]. Expert Systems with Applications, 2016, 64:239-246.

[5] 姚德臣, 杨建伟, 程晓卿,等. 基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J]. 机械工程学报, 2018, 54(9):168-176.

Yao Dechen, Yang Jianwei, Cheng Xiaoqing, et al. Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier[J]. Journal of Mechanical Engineering, 2018,54(9):168-176.

[6] Hulse J V, Khoshgoftaar T M, Napolitano A. Experimental perspectives on learning from imbalanced data[C]// Machine Learning, Proceedings of the Twenty-Fourth International Conference. DBLP, 2007:935-942.

[7] Wang X. An Innovative SVM for Wheat Seed Quality Estimation[J]. Journal of Information & Computational Science, 2015, 12(1):223-233.

[8] Veropoulos K, Campbell C, Cristianini N. Controlling the Sen- sitivity of Support Vector Machines[C]// Proceedings of the International Joint Conference on Artificial Intelligence.Stockholm：IJCAI，1999:55-60.

[9] Lin C F, Wang S D. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):464- 471.

[10] Batuwita R, Palade V. FSVM-CIL: Fuzzy Support Vector Ma- chines for Class Imbalance Learning[J]. IEEE Transactions on Fuzzy Systems,2010,18(3):558-571.

[11] 鞠哲, 曹隽喆, 顾宏. 用于不平衡数据分类的模糊支持向量机算法[J]. 大连理工大学学报, 2016, 56(5):525-531.

Ju Zhe,Cao Juanzhe,Gu Hong.A fuzzy Support vector machine algorithm for unbalanced data classification[J]. Journal of Dali- an University of Technology,2016,56 (5):525 -531.

[12] Batuwita R, Palade V. Efficient resampling methods for train- ing support vector machines with imbalanced datasets[C] // Int- ernational Joint Conference on Neural Networks. IEEE, 2010: 1-8.

[13] Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Inte- lligence Research, 2002, 16(1):321-357.

[14] Han H, Wang W Y, Mao B H. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learnig[J]. Lecture Notes in Co mputer Science, 2005, 3644 (5) :878-887.

[15] 陶新民,张冬雪,郝思媛,等.基于谱聚类下采样失衡数据下SVM故障检测[J].振动与冲击, 2013, 32(16):30-36.

Tao Xinmin,Zhang Dongxue,Hao Siyuan,et all.Fault detection based on spectral clustering combined with under-sampling SVM under unbalanced datasets[J].Journa l of Vibration and Shock , 2013, 32(16):30-36.

[16] Wang Q, Luo Z, Huang J, et al. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM[J].Computational intelligence and neuroscience, 2017, 2017(3):1827016,11pages,https://doi.org/10.1155/2017/1827016.

[17] Ma L, & Fan S. CURE-SMOTE algorithm and hybrid algorithm forfeature selection and parameter optimization based on random forests[J].BMC Bioinformatics, 2017,18(1), 169. https: //doi .org /10.1186/s12859-017-1578-z.

[18] Cortes C, Vapnik V. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297.

[19] Yu H , Mu C , Sun C , et al. Support vector machine-based optimized decision threshold adjustment strategy for classifying imbalanced data[J]. Knowledge-Based Systems, 2015, 76(1):67-78.

[20] Barua S , Islam M M , Yao X , et al. MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2):405-425.

[21] 朱坚,杨博,王永健,唐晓婕,李宏光.一种新型的基于莱文斯坦距离层次聚类的时序操作优化方法[J/OL].化工学报:1-10[2018-12-09].http://kns.cnki.net/kcms/detail/11.1946.TQ.20181204.1726.134.html.

Zhu J,Yang B,Wang Y J,Li X G.A New Operation Optimization Method with Time Series Based on Levenshtein Distance Hierarchical Clustering [J/OL].Journal of Chemical Industry and Engineering(China):1-10[2018-12-09].http://kns.cnki.net/kcms/detail/11.1 946 .TQ .20181204.1726.134.html.

[22] Zhou S , Xu Z , Liu F . Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering[J]. IEEE Transactions on Neural Networks & Learning Systems, 2017, 28(12):3007-3017.

[23] 黄海松,魏建安,康佩栋.基于不平衡数据样本特性的新型过采样SVM分类算法[J].控制与决策,2018(09):1549-1558.

Huang Haisong,Wei Jianan,Kang Peidong.A New Over-sampling SVM Classification Algorithm Based on Unbalanced Data Sample Characteristics[J]Control and Decision,2018 (09) :15 49-1558.

[24] 陶新民,张冬雪,郝思媛,等.基于谱聚类下采样失衡数据下SVM故障检测[J].振动与冲击, 2013, 32(16):30-36.

[25] Mao W, He L, Yan Y, et al. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine[J]. Mechanical Systems & Signal Processing, 2017, 83:450-473.

[26] Geng X, Zhan D C, Zhou Z H. Supervised nonlinear dimensionality reduction for visualization and classification[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2005, 3 5(6):1098:1107.

[27] Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise [C] // IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2011:4144-4147.

[28] Lei Y, Liu Z, Ouazri J, et al. A fault diagnosis method of rolling element bearings based on CEEMDAN[J]. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science 1989-1996 (vols 203-210), 2015, 231(10):1-12.

[29] Colominas M A, Schlotthauer G, Torres M E. Improved comp- lete ensemble EMD: A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing & Control, 2014, 14(1):19-29.

[30] 蒋永华, 程光明, 阚君武,等. 基于NGA优化SVM的滚动轴承故障诊断[J]. 仪器仪表学报, 2013, 34(12):2684 -2689.

Jiang Yonghua ,Chen Guangmin,Kan Junwu,et all.Rolling bear- ing fault diagnosis based on NGA optimized SVM[J]. Chinese Journal of Scientific Instrument, 2013, 34(12):2684- 2689.

[31] Bearing Data Center Website.Case Western Reserve Unive- rsity[EB/OL].[2018-07-19].http:/www.eecs.Cwru.edu/laboratory/ bearing.

[32] 段礼祥, 郭晗, 王金江. 数据集不均衡下的设备故障程度识别方法研究[J]. 振动与冲击, 2016, 35(20):178-182.

Duan Lixiang,Guo Han,Wang Jinjiang.A mechanical fault severity identification method under unbalanced datasets [J].Journal of Vibration and Shock ,2016,35 (20):178-182