基于特征迁移学习的变工况下滚动轴承故障诊断方法
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摘要
针对滚动轴承尤其是变工况条件下很难或无法获取大量带标签的振动数据,以致诊断准确率低的问题,提出一种基于变分模态分解(variationalmodedecomposition,VMD)及多特征构造和迁移学习相结合的滚动轴承故障诊断方法。该方法利用VMD对滚动轴承各状态振动信号进行分解,得到一系列固有模态函数,对其构成的矩阵进行奇异值分解求奇异值及奇异值熵,再结合振动信号的时域、频域特征构造多特征集。同时引入半监督迁移成分分析方法(semisupervised transfer component analysis,SSTCA),并对其核函数进行多核构造,将不同工况样本特征共同映射到一个共享再生核Hilbert空间,进而提高数据类内紧凑性和类间区分性。采用最大均值差异嵌入法选择更有效的数据作为源域,将源域特征样本输入支持向量机(supportvectormachine,SVM)进行训练,测试映射后的目标域特征样本。实验表明,所提多核SSTCA-SVM方法与其他方法相比较,在变工况下滚动轴承多状态分类中具有更高准确率。
For a rolling bearing, especially under variable working conditions, it is difficult and even unable to obtain a large number of tagged vibration data, and then the fault diagnosis accuracy is low, a new fault diagnosis method of the rolling bearings is proposed based on variational mode decomposition(VMD) and multiple feature structure combined with transfer learning. By using VMD, each state vibration signal of the rolling bearing is decomposed into a series of intrinsic mode function(IMF) components, then the singular values of the IMF matrix can be obtained by singular value decomposition, and the singular value entropy can be calculated. The multiple feature set is constructed with the singular values, the singular value entropy, the time domain and frequency domain features. At the same time, semisupervised transfer component analysis(SSTCA) is introduced, and its kernel is constructed as the multi-kernel function. The sample features of different working conditions are all mapped to a shared reproducing kernel Hilbert space, and which can improve the compactness and interclass distinction of the data. By using the maximum mean discrepancy embedding method, more effective data are selected and regarded as the source domain. The source domain feature samples are input into support vector machine(SVM), and then the feature samples of the mapped target domain are tested. The experimental results show that, compared with other methods, the proposed multi-kernel SSTCA-SVM method has a higher accuracy in the multi-state classification of the rolling bearings under variable working conditions.
For a rolling bearing, especially under variable working conditions, it is difficult and even unable to obtain a large number of tagged vibration data, and then the fault diagnosis accuracy is low, a new fault diagnosis method of the rolling bearings is proposed based on variational mode decomposition(VMD) and multiple feature structure combined with transfer learning. By using VMD, each state vibration signal of the rolling bearing is decomposed into a series of intrinsic mode function(IMF) components, then the singular values of the IMF matrix can be obtained by singular value decomposition, and the singular value entropy can be calculated. The multiple feature set is constructed with the singular values, the singular value entropy, the time domain and frequency domain features. At the same time, semisupervised transfer component analysis(SSTCA) is introduced, and its kernel is constructed as the multi-kernel function. The sample features of different working conditions are all mapped to a shared reproducing kernel Hilbert space, and which can improve the compactness and interclass distinction of the data. By using the maximum mean discrepancy embedding method, more effective data are selected and regarded as the source domain. The source domain feature samples are input into support vector machine(SVM), and then the feature samples of the mapped target domain are tested. The experimental results show that, compared with other methods, the proposed multi-kernel SSTCA-SVM method has a higher accuracy in the multi-state classification of the rolling bearings under variable working conditions.
引文
[1]王玉静,那晓栋,康守强,等.基于EEMD-Hilbert包络谱和DBN的变负载下滚动轴承状态识别方法[J].中国电机工程学报,2017,37(23):6943-6950.Wang Yujing,Na Xiaodong,Kang Shouqiang,et al.State recognition method of a rolling bearing based on EEMD-Hilbert envelope spectrum and DBN under variable load[J].Proceedings of the CSEE,2017,37(23):6943-6950(in Chinese).
[2]Liu Hongmei,Wang Xuan,Lu Chen.Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition[J].Mathematical Problems in Engineering,2014:765621.
[3]Wu T Y,Yu C L,Liu D C.On multi-scale entropy analysis of order-tracking measurement for bearing fault diagnosis under variable speed[J].Entropy,2016,18(8):292.
[4]Baraldi P,Cannarile F,Di Maio F,et al.Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions[J].Engineering Applications of Artificial Intelligence,2016(56):1-13.
[5]Fei Shengwei.Fault diagnosis of bearing under varying load conditions by utilizing composite features self-adaptive reduction-based RVM classifier[J].Journal of Vibration Engineering&Technologies,2017,5(3):269-276.
[6]Pan S J,Yang Qiang.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[7]庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.Zhuang Fuzhen,Luo Ping,He Qing,et al.Survey on transfer learning research[J].Journal of Software,2015,26(1):26-39(in Chinese).
[8]沈飞,陈超,严如强.奇异值分解与迁移学习在电机故障诊断中的应用[J].振动工程学报,2017,30(1):118-126.Shen Fei,Chen Chao,Yan Ruqiang.Application of SVDand transfer learning strategy on motorfault diagnosis[J].Journal of Vibration Engineering,2017,30(1):118-126(in Chinese).
[9]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 Ruqiang.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(in Chinese).
[10]韩敏,杨雪.改进贝叶斯ARTMAP的迁移学习遥感影像分类算法[J].电子学报,2016,44(9):2248-2253.Han Min,Yang Xue.Transfer learning using improved Bayesian ARTMAP for remote sensing image classification[J].Acta Electronica Sinica,2016,44(9):2248-2253(in Chinese).
[11]Dai Wenyuan,Yang Qiang,Xue Guirong,et al.Boosting for transfer learning[C]//Proceedings of the 24th International Conference on Machine Learning.Corvalis,Oregon,USA:ACM,2007:193-200.
[12]Matasci G,Volpi M,Kanevski M,et al.Semisupervised transfer component analysis for domain adaptation in remote sensing image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(7):3550-3564.
[13]Von Bünau P,Meinecke F C,Király F C,et al.Finding stationary subspaces in multivariate time series[J].Physical Review Letters,2009,103(21):214101.
[14]Shi Yuan,Sha Fei.Information-theoretical learning of discriminative clusters for unsupervised domain adaptation[C]//Proceedings of the 29th International Coference on International Conference on Machine Learning.Edinburgh,Scotland:Omnipress,2012:1275-1282.
[15]Gong Boqing,Shi Yuan,Sha Fei,et al.Geodesic flow kernel for unsupervised domain adaptation[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI,USA:IEEE,2012:2066-2073.
[16]Fernando B,Habrard A,Sebban M,et al.Unsupervised visual domain adaptation using subspace alignment[C]//Proceedings of 2013 IEEE International Conference on Computer Vision.Sydney,NSW,Australia:IEEE,2013:2960-2967.
[17]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.Liu Changliang,Wu Yingjie,Zhen Chenggang.Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J].Proceedings of the CSEE,2015,35(13):3358-3365(in Chinese).
[18]赵学智,陈统坚,叶邦彦.奇异值分解对连续Morlet小波变换的压缩和提纯[J].机械工程学报,2015,51(16):57-70.Zhao Xuezhi,Chen Tongjian,Ye Bangyan.Purification and compression of continuous morlet wavelet transform based on singular value decomposition[J].Journal of Mechanical Engineering,2015,51(16):57-70(in Chinese).
[19]毋文峰,李浩,朱露.基于奇异值融合的机械设备盲信息提取[J].中国机械工程,2015,26(22):3028-3033.Wu Wenfeng,Li Hao,Zhu Lu.Blind mechanical information extraction based on singular value fusion[J].China Mechanical Engineering,2015,26(22):3028-3033(in Chinese).
[20]康守强,王玉静,崔历历,等.基于CFOA-MKHSVM的滚动轴承健康状态评估方法[J].仪器仪表学报,2016,37(9):2029-2035.Kang Shouqiang,Wang Yujing,Cui Lili,et al.Health state assessment of a rolling bearing based on CFOA-MKHSVM method[J].Chinese Journal of Scientific Instrument,2016,37(9):2029-2035(in Chinese).
[21]Damodaran B B,Courty N,Lefèvre S.Sparse Hilbert Schmidt independence criterion and surrogate-kernelbased feature selection for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(4):2385-2398.
[22]Kang Shouqiang,Ma Danyang,Wang Yujing,et al.Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding[J].Mechanical Systems and Signal Processing,2017(86):40-57.
[23]Yan Ke,Kou Lu,Zhang D.Learning domain-invariant subspace using domain features and independence maximization[J].IEEE Transactions on Cybernetics,2018,48(1):288-299.
[2]Liu Hongmei,Wang Xuan,Lu Chen.Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition[J].Mathematical Problems in Engineering,2014:765621.
[3]Wu T Y,Yu C L,Liu D C.On multi-scale entropy analysis of order-tracking measurement for bearing fault diagnosis under variable speed[J].Entropy,2016,18(8):292.
[4]Baraldi P,Cannarile F,Di Maio F,et al.Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions[J].Engineering Applications of Artificial Intelligence,2016(56):1-13.
[5]Fei Shengwei.Fault diagnosis of bearing under varying load conditions by utilizing composite features self-adaptive reduction-based RVM classifier[J].Journal of Vibration Engineering&Technologies,2017,5(3):269-276.
[6]Pan S J,Yang Qiang.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[7]庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.Zhuang Fuzhen,Luo Ping,He Qing,et al.Survey on transfer learning research[J].Journal of Software,2015,26(1):26-39(in Chinese).
[8]沈飞,陈超,严如强.奇异值分解与迁移学习在电机故障诊断中的应用[J].振动工程学报,2017,30(1):118-126.Shen Fei,Chen Chao,Yan Ruqiang.Application of SVDand transfer learning strategy on motorfault diagnosis[J].Journal of Vibration Engineering,2017,30(1):118-126(in Chinese).
[9]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 Ruqiang.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(in Chinese).
[10]韩敏,杨雪.改进贝叶斯ARTMAP的迁移学习遥感影像分类算法[J].电子学报,2016,44(9):2248-2253.Han Min,Yang Xue.Transfer learning using improved Bayesian ARTMAP for remote sensing image classification[J].Acta Electronica Sinica,2016,44(9):2248-2253(in Chinese).
[11]Dai Wenyuan,Yang Qiang,Xue Guirong,et al.Boosting for transfer learning[C]//Proceedings of the 24th International Conference on Machine Learning.Corvalis,Oregon,USA:ACM,2007:193-200.
[12]Matasci G,Volpi M,Kanevski M,et al.Semisupervised transfer component analysis for domain adaptation in remote sensing image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(7):3550-3564.
[13]Von Bünau P,Meinecke F C,Király F C,et al.Finding stationary subspaces in multivariate time series[J].Physical Review Letters,2009,103(21):214101.
[14]Shi Yuan,Sha Fei.Information-theoretical learning of discriminative clusters for unsupervised domain adaptation[C]//Proceedings of the 29th International Coference on International Conference on Machine Learning.Edinburgh,Scotland:Omnipress,2012:1275-1282.
[15]Gong Boqing,Shi Yuan,Sha Fei,et al.Geodesic flow kernel for unsupervised domain adaptation[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI,USA:IEEE,2012:2066-2073.
[16]Fernando B,Habrard A,Sebban M,et al.Unsupervised visual domain adaptation using subspace alignment[C]//Proceedings of 2013 IEEE International Conference on Computer Vision.Sydney,NSW,Australia:IEEE,2013:2960-2967.
[17]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.Liu Changliang,Wu Yingjie,Zhen Chenggang.Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J].Proceedings of the CSEE,2015,35(13):3358-3365(in Chinese).
[18]赵学智,陈统坚,叶邦彦.奇异值分解对连续Morlet小波变换的压缩和提纯[J].机械工程学报,2015,51(16):57-70.Zhao Xuezhi,Chen Tongjian,Ye Bangyan.Purification and compression of continuous morlet wavelet transform based on singular value decomposition[J].Journal of Mechanical Engineering,2015,51(16):57-70(in Chinese).
[19]毋文峰,李浩,朱露.基于奇异值融合的机械设备盲信息提取[J].中国机械工程,2015,26(22):3028-3033.Wu Wenfeng,Li Hao,Zhu Lu.Blind mechanical information extraction based on singular value fusion[J].China Mechanical Engineering,2015,26(22):3028-3033(in Chinese).
[20]康守强,王玉静,崔历历,等.基于CFOA-MKHSVM的滚动轴承健康状态评估方法[J].仪器仪表学报,2016,37(9):2029-2035.Kang Shouqiang,Wang Yujing,Cui Lili,et al.Health state assessment of a rolling bearing based on CFOA-MKHSVM method[J].Chinese Journal of Scientific Instrument,2016,37(9):2029-2035(in Chinese).
[21]Damodaran B B,Courty N,Lefèvre S.Sparse Hilbert Schmidt independence criterion and surrogate-kernelbased feature selection for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(4):2385-2398.
[22]Kang Shouqiang,Ma Danyang,Wang Yujing,et al.Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding[J].Mechanical Systems and Signal Processing,2017(86):40-57.
[23]Yan Ke,Kou Lu,Zhang D.Learning domain-invariant subspace using domain features and independence maximization[J].IEEE Transactions on Cybernetics,2018,48(1):288-299.