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Volumn 89, Issue , 2015, Pages 56-85

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

Author keywords

Ensemble; Incremental; Intelligent fault diagnosis; Multivariable; Roller bearing; Support vector machine

Indexed keywords

FAILURE ANALYSIS; INTELLIGENT SYSTEMS; ROLLER BEARINGS; SUPPORT VECTOR MACHINES;

EID: 84944355420     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.06.017     Document Type: Article
Times cited : (163)

References (40)
  • 1
    • 61849120773 scopus 로고    scopus 로고
    • Difference histograms: A new tool for time series analysis applied to bearing fault diagnosis
    • April 15
    • B.J. van Wyk, M.A. van Wyk, and G. Qi Difference histograms: a new tool for time series analysis applied to bearing fault diagnosis Pattern Recogn. Lett. 30 6 2009 595 599 April 15
    • (2009) Pattern Recogn. Lett. , vol.30 , Issue.6 , pp. 595-599
    • Van Wyk, B.J.1    Van Wyk, M.A.2    Qi, G.3
  • 2
    • 84881188735 scopus 로고    scopus 로고
    • Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal
    • M. Singh, and R. Kumar Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal Measurement 46 9 November 2013 3508 3515
    • (2013) Measurement , vol.46 , Issue.9 , pp. 3508-3515
    • Singh, M.1    Kumar, R.2
  • 3
    • 84884341697 scopus 로고    scopus 로고
    • An algorithm for improving the coefficient accuracy of wavelet packet analysis
    • X.T. Jiao, K. Ding, and G.L. He An algorithm for improving the coefficient accuracy of wavelet packet analysis Measurement 47 January 2014 207 220
    • (2014) Measurement , vol.47 , pp. 207-220
    • Jiao, X.T.1    Ding, K.2    He, G.L.3
  • 4
    • 84883544649 scopus 로고    scopus 로고
    • Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine
    • S.J. Dong, B.P. Tang, and R.X. Chen Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine Measurement 46 10 December 2013 4189 4199
    • (2013) Measurement , vol.46 , Issue.10 , pp. 4189-4199
    • Dong, S.J.1    Tang, B.P.2    Chen, R.X.3
  • 5
    • 84870240076 scopus 로고    scopus 로고
    • Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal
    • R. Kumar, and M. Singh Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal Measurement 46 1 January 2013 537 545
    • (2013) Measurement , vol.46 , Issue.1 , pp. 537-545
    • Kumar, R.1    Singh, M.2
  • 6
    • 84890306702 scopus 로고    scopus 로고
    • Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis
    • J.H. Yan, and L. Lu Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis Signal Process. 98 May 2014 74 87
    • (2014) Signal Process. , vol.98 , pp. 74-87
    • Yan, J.H.1    Lu, L.2
  • 7
    • 84934970878 scopus 로고    scopus 로고
    • Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition
    • H.M. Liu, X. Wang, and C. Lu Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition Math. Probl. Eng. 2014
    • (2014) Math. Probl. Eng.
    • Liu, H.M.1    Wang, X.2    Lu, C.3
  • 8
    • 84880714051 scopus 로고    scopus 로고
    • An enhanced Hilbert-Huang transform technique for bearing condition monitoring
    • S. Osman, and W. Wang An enhanced Hilbert-Huang transform technique for bearing condition monitoring Meas. Sci. Technol. 24 8 August 2013
    • (2013) Meas. Sci. Technol. , vol.24 , Issue.8
    • Osman, S.1    Wang, W.2
  • 9
    • 3943082051 scopus 로고    scopus 로고
    • Pattern recognition for automatic machinery fault diagnosis
    • Q. Sun, P. Chen, D.J. Zhang, and et al. Pattern recognition for automatic machinery fault diagnosis J. Vib. Acoust.-Trans. Asme 126 2 April 2004 307 316
    • (2004) J. Vib. Acoust.-Trans. Asme , vol.126 , Issue.2 , pp. 307-316
    • Sun, Q.1    Chen, P.2    Zhang, D.J.3
  • 10
    • 84883383725 scopus 로고    scopus 로고
    • Multi-steps degradation process prediction for bearing based on improved back propagation neural network
    • L. Mi, W. Tan, and R. Chen Multi-steps degradation process prediction for bearing based on improved back propagation neural network Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. 227 7 July 2013 1544 1553
    • (2013) Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. , vol.227 , Issue.7 , pp. 1544-1553
    • Mi, L.1    Tan, W.2    Chen, R.3
  • 11
    • 84884546310 scopus 로고    scopus 로고
    • Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics
    • G.S. Vijay, S.P. Pai, N.S. Sriram, and et al. Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics Proc. Instit. Mech. Eng. Part J-J. Eng. Tribol. 227 J6 June 2013 640 653
    • (2013) Proc. Instit. Mech. Eng. Part J-J. Eng. Tribol. , vol.227 , Issue.6 , pp. 640-653
    • Vijay, G.S.1    Pai, S.P.2    Sriram, N.S.3
  • 12
    • 84867846656 scopus 로고    scopus 로고
    • A rule-based intelligent method for fault diagnosis of rotating machinery
    • D.Y. Dou, J.G. Yang, J.T. Liu, and et al. A rule-based intelligent method for fault diagnosis of rotating machinery Knowl.-Based Syst. 36 December 2012 1 8
    • (2012) Knowl.-Based Syst. , vol.36 , pp. 1-8
    • Dou, D.Y.1    Yang, J.G.2    Liu, J.T.3
  • 13
    • 77951204473 scopus 로고    scopus 로고
    • On the extraction of rules in the identification of bearing defects in rotating machinery using decision tree
    • M. Boumahdi, J.P. Dron, S. Rechak, and et al. On the extraction of rules in the identification of bearing defects in rotating machinery using decision tree Expert Syst. Appl. 37 8 August 2010 5887 5894
    • (2010) Expert Syst. Appl. , vol.37 , Issue.8 , pp. 5887-5894
    • Boumahdi, M.1    Dron, J.P.2    Rechak, S.3
  • 14
    • 34047251878 scopus 로고    scopus 로고
    • Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing
    • V. Sugumaran, and K.I. Ramachandran Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing Mech. Syst. Signal Process. 21 5 July 2007 2237 2247
    • (2007) Mech. Syst. Signal Process. , vol.21 , Issue.5 , pp. 2237-2247
    • Sugumaran, V.1    Ramachandran, K.I.2
  • 15
    • 84901238143 scopus 로고    scopus 로고
    • Binary PSO with mutation operator for feature selection using decision tree applied to spam detection
    • Y.D. Zhang, S.H. Wang, P. Phillips, and et al. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection Knowl.-Based Syst. 64 July 2014 22 31
    • (2014) Knowl.-Based Syst. , vol.64 , pp. 22-31
    • Zhang, Y.D.1    Wang, S.H.2    Phillips, P.3
  • 16
    • 84859433868 scopus 로고    scopus 로고
    • Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine
    • Y.J. Wang, S.Q. Kang, Y.C. Jiang, and et al. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine Mech. Syst. Signal Process. 29 May 2012 404 414
    • (2012) Mech. Syst. Signal Process. , vol.29 , pp. 404-414
    • Wang, Y.J.1    Kang, S.Q.2    Jiang, Y.C.3
  • 17
    • 84885342190 scopus 로고    scopus 로고
    • Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization
    • F.F. Chen, B.P. Tang, T. Song, and et al. Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization Measurement 47 January 2014 576 590
    • (2014) Measurement , vol.47 , pp. 576-590
    • Chen, F.F.1    Tang, B.P.2    Song, T.3
  • 18
    • 77955232331 scopus 로고    scopus 로고
    • Multi-fault classification based on support vector machine trained by chaos particle swarm optimization
    • X.L. Tang, L. Zhuang, J. Cai, and et al. Multi-fault classification based on support vector machine trained by chaos particle swarm optimization Knowl.-Based Syst. 23 5 July 2010 486 490
    • (2010) Knowl.-Based Syst. , vol.23 , Issue.5 , pp. 486-490
    • Tang, X.L.1    Zhuang, L.2    Cai, J.3
  • 21
    • 13844281522 scopus 로고    scopus 로고
    • Incremental training of support vector machines
    • A. Shilton, M. Palaniswami, D. Ralph, and et al. Incremental training of support vector machines IEEE Trans. Neural Networks 16 1 January 2005 114 131
    • (2005) IEEE Trans. Neural Networks , vol.16 , Issue.1 , pp. 114-131
    • Shilton, A.1    Palaniswami, M.2    Ralph, D.3
  • 22
    • 0035521110 scopus 로고    scopus 로고
    • Learn++: An incremental learning algorithm for supervised neural networks
    • R. Polikar, L. Udpa, S.S. Udpa, and et al. Learn++: an incremental learning algorithm for supervised neural networks IEEE Trans. Syst. Man Cybernetics Part C-Appl. Rev. 31 4 November 2001 497 508
    • (2001) IEEE Trans. Syst. Man Cybernetics Part C-Appl. Rev. , vol.31 , Issue.4 , pp. 497-508
    • Polikar, R.1    Udpa, L.2    Udpa, S.S.3
  • 23
    • 33745777639 scopus 로고    scopus 로고
    • Incremental support vector learning: Analysis, implementation and applications
    • P. Laskov, C. Gehl, S. Kruger, and et al. Incremental support vector learning: analysis, implementation and applications J. Mach. Learn. Res. 7 September 2006 1909 1936
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1909-1936
    • Laskov, P.1    Gehl, C.2    Kruger, S.3
  • 24
    • 34047104426 scopus 로고    scopus 로고
    • An ensemble-based incremental learning approach to data fusion
    • D. Parikh, and R. Polikar An ensemble-based incremental learning approach to data fusion IEEE Trans. Syst. Man Cybern. Part B-Cybern. 37 2 April 2007 437 450
    • (2007) IEEE Trans. Syst. Man Cybern. Part B-Cybern. , vol.37 , Issue.2 , pp. 437-450
    • Parikh, D.1    Polikar, R.2
  • 27
    • 84922450521 scopus 로고    scopus 로고
    • Constructing a multi-class classifier using one-against-one approach with different binary classifiers
    • February 3
    • S. Kang, S. Cho, and P. Kang Constructing a multi-class classifier using one-against-one approach with different binary classifiers Neurocomputing 149 2015 677 682 February 3
    • (2015) Neurocomputing , vol.149 , pp. 677-682
    • Kang, S.1    Cho, S.2    Kang, P.3
  • 28
    • 0036505670 scopus 로고    scopus 로고
    • A comparison of methods for multiclass support vector machines
    • C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Trans. Neural Networks 13 2 March 2002 415 425
    • (2002) IEEE Trans. Neural Networks , vol.13 , Issue.2 , pp. 415-425
    • Hsu, C.W.1    Lin, C.J.2
  • 29
    • 72449154589 scopus 로고    scopus 로고
    • A review on the combination of binary classifiers in multiclass problems
    • A.C. Lorena, A.C.P.L.F. de Carvalho, and J.M.P. Gama A review on the combination of binary classifiers in multiclass problems Artif. Intell. Rev. 30 1-4 December 2008 19 37
    • (2008) Artif. Intell. Rev. , vol.30 , Issue.1-4 , pp. 19-37
    • Lorena, A.C.1    De Carvalho, A.C.P.L.F.2    Gama, J.M.P.3
  • 30
    • 79953051509 scopus 로고    scopus 로고
    • An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
    • M. Galar, A. Fernandez, E. Barrenechea, and et al. An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes Pattern Recogn. 44 8 August 2011 1761 1776
    • (2011) Pattern Recogn. , vol.44 , Issue.8 , pp. 1761-1776
    • Galar, M.1    Fernandez, A.2    Barrenechea, E.3
  • 31
    • 34047251505 scopus 로고    scopus 로고
    • Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAS
    • Y. Lei, Z. He, Y. Zi, and et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAS Mech. Syst. Signal Process. 21 5 July 2007 2280 2294
    • (2007) Mech. Syst. Signal Process. , vol.21 , Issue.5 , pp. 2280-2294
    • Lei, Y.1    He, Z.2    Zi, Y.3
  • 32
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting J. Comput. Syst. Sci. 55 1 August 1997 119 139
    • (1997) J. Comput. Syst. Sci. , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 33
    • 84944351357 scopus 로고    scopus 로고
    • http://www.eecs.case.edu/laboratory/bearing/.
  • 34
    • 58949093594 scopus 로고    scopus 로고
    • Fault diagnosis based on Walsh transform and rough sets
    • X.Q. Xiang, J.Z. Zhou, C.S. Li, and et al. Fault diagnosis based on Walsh transform and rough sets Mech. Syst. Signal Process. 23 4 May 2009 1313 1326
    • (2009) Mech. Syst. Signal Process. , vol.23 , Issue.4 , pp. 1313-1326
    • Xiang, X.Q.1    Zhou, J.Z.2    Li, C.S.3
  • 35
    • 67349287589 scopus 로고    scopus 로고
    • A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique
    • Z.B. Xu, J.P. Xuan, T.L. Shi, and et al. A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique Expert Syst. Appl. 36 9 November 2009 11801 11807
    • (2009) Expert Syst. Appl. , vol.36 , Issue.9 , pp. 11801-11807
    • Xu, Z.B.1    Xuan, J.P.2    Shi, T.L.3
  • 36
    • 64049115475 scopus 로고    scopus 로고
    • Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing
    • Z.B. Xu, J.P. Xuan, T.L. Shi, and et al. Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing Expert Syst. Appl. 36 6 August 2009 9961 9968
    • (2009) Expert Syst. Appl. , vol.36 , Issue.6 , pp. 9961-9968
    • Xu, Z.B.1    Xuan, J.P.2    Shi, T.L.3
  • 37
    • 84919933756 scopus 로고    scopus 로고
    • Heterogeneous feature models and feature selection applied to bearing fault diagnosis
    • T.W. Rauber, F.D. Boldt, and F.M. Varejao Heterogeneous feature models and feature selection applied to bearing fault diagnosis IEEE Trans. Industr. Electron. 62 1 January 2015 637 646
    • (2015) IEEE Trans. Industr. Electron. , vol.62 , Issue.1 , pp. 637-646
    • Rauber, T.W.1    Boldt, F.D.2    Varejao, F.M.3
  • 38
    • 78650195131 scopus 로고    scopus 로고
    • An ACO-based algorithm for parameter optimization of support vector machines
    • X.L. Zhang, X.F. Chen, and Z.J. He An ACO-based algorithm for parameter optimization of support vector machines Expert Syst. Appl. 37 9 September 2010 6618 6628
    • (2010) Expert Syst. Appl. , vol.37 , Issue.9 , pp. 6618-6628
    • Zhang, X.L.1    Chen, X.F.2    He, Z.J.3
  • 39
    • 76349102342 scopus 로고    scopus 로고
    • Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm
    • X.L. Zhang, X.F. Chen, and Z.J. He Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. 224 C1 2010 217 229
    • (2010) Proc. Instit. Mech. Eng. Part C-J. Mech. Eng. Sci. , vol.224 , Issue.1 , pp. 217-229
    • Zhang, X.L.1    Chen, X.F.2    He, Z.J.3
  • 40
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demsar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 January 2006 1 30
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1-30
    • Demsar, J.1


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