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Volumn 14, Issue 7, 2018, Pages 3261-3270

Sparse Deep Stacking Network for Fault Diagnosis of Motor

Author keywords

Deep stacking network (DSN); kernel method; motor fault diagnosis; sparse deep learning; sparse regularization

Indexed keywords

FAILURE ANALYSIS; FAULT DETECTION;

EID: 85044378737     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2018.2819674     Document Type: Article
Times cited : (178)

References (31)
  • 1
    • 29244467991 scopus 로고    scopus 로고
    • Condition monitoring and fault diagnosis of electrical motors-A review
    • Dec.
    • S. Nandi, H. A. Toliyat, and X. Li, "Condition monitoring and fault diagnosis of electrical motors-A review, " IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719-729, Dec. 2005.
    • (2005) IEEE Trans. Energy Convers. , vol.20 , Issue.4 , pp. 719-729
    • Nandi, S.1    Toliyat, H.A.2    Li, X.3
  • 2
    • 85041196149 scopus 로고    scopus 로고
    • Nonconvex sparse regularization and convex optimization for bearing fault diagnosis
    • S. Wang, I. Selesnick, G. Cai, Y. Feng, X. Sui, and X. Chen, "Nonconvex sparse regularization and convex optimization for bearing fault diagnosis, " IEEE Trans. Ind. Electron., vol. 65, no. 9, pp. 7332-7342, 2018.
    • (2018) IEEE Trans. Ind. Electron. , vol.65 , Issue.9 , pp. 7332-7342
    • Wang, S.1    Selesnick, I.2    Cai, G.3    Feng, Y.4    Sui, X.5    Chen, X.6
  • 3
    • 84957801663 scopus 로고    scopus 로고
    • Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing
    • C. Sun, Z. Zhang and X. Luo, "Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing, " J. Sound Vib., vol. 368, pp. 249-263, 2016.
    • (2016) J. Sound Vib. , vol.368 , pp. 249-263
    • Sun, C.1    Zhang, Z.2    Luo, X.3
  • 4
    • 0034295565 scopus 로고    scopus 로고
    • Recent developments of induction motor drives fault diagnosis using AI techniques
    • F. Filippetti, G. Franceschini, and C. Tassoni, "Recent developments of induction motor drives fault diagnosis using AI techniques, " IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 994-1004, 2000.
    • (2000) IEEE Trans. Ind. Electron. , vol.47 , Issue.5 , pp. 994-1004
    • Filippetti, F.1    Franceschini, G.2    Tassoni, C.3
  • 5
    • 79956155898 scopus 로고    scopus 로고
    • Bearing fault detection of induction motor using wavelet and support vector machines (SVMs)
    • P. Konar and P. Chattopadhyay, "Bearing fault detection of induction motor using wavelet and support vector machines (SVMs), " Appl. Soft Comput., vol. 11, no. 6, pp. 4203-4211, 2011.
    • (2011) Appl. Soft Comput. , vol.11 , Issue.6 , pp. 4203-4211
    • Konar, P.1    Chattopadhyay, P.2
  • 6
    • 84865606945 scopus 로고    scopus 로고
    • Multi-sensor data fusion using support vector machine for motor fault detection
    • T. P. Banerjee and S. Das, "Multi-sensor data fusion using support vector machine for motor fault detection, " Inf. Sci., vol. 217, pp. 96-107, 2012.
    • (2012) Inf. Sci. , vol.217 , pp. 96-107
    • Banerjee, T.P.1    Das, S.2
  • 7
    • 81855170055 scopus 로고    scopus 로고
    • Induction motors bearing fault detection using pattern recognition techniques
    • J. Zarei, "Induction motors bearing fault detection using pattern recognition techniques, " Expert Syst. Appl., vol. 39, no. 1, pp. 68-73, 2012.
    • (2012) Expert Syst. Appl. , vol.39 , Issue.1 , pp. 68-73
    • Zarei, J.1
  • 8
    • 0034297837 scopus 로고    scopus 로고
    • Neural-network-based motor rolling bearing fault diagnosis
    • Oct.
    • B. Li, M. Y. Chow, Y. Tipsuwan, and J. C. Hung, "Neural-network-based motor rolling bearing fault diagnosis, " IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1060-1069, Oct. 2000.
    • (2000) IEEE Trans. Ind. Electron. , vol.47 , Issue.5 , pp. 1060-1069
    • Li, B.1    Chow, M.Y.2    Tipsuwan, Y.3    Hung, J.C.4
  • 9
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning
    • Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning, " Nature, vol. 521, no. 7553, pp. 436-444, 2015.
    • (2015) Nature , vol.521 , Issue.7553 , pp. 436-444
    • LeCun, Y.1    Bengio, Y.2    Hinton, G.3
  • 10
    • 85042082491 scopus 로고    scopus 로고
    • A review on the application of deep learning in system health management
    • S. Khan, and T. Yairi, "A review on the application of deep learning in system health management, " Mech. Syst. Signal Process., vol. 107, pp. 241-265, 2018.
    • (2018) Mech. Syst. Signal Process. , vol.107 , pp. 241-265
    • Khan, S.1    Yairi, T.2
  • 11
    • 84957837518 scopus 로고    scopus 로고
    • Deep learning for visual understanding: A review
    • Y. Guo, Y. Liu, and A. Oerlemans, "Deep learning for visual understanding: A review, " Neurocomputing, vol. 187, pp. 27-48, 2016.
    • (2016) Neurocomputing , vol.187 , pp. 27-48
    • Guo, Y.1    Liu, Y.2    Oerlemans, A.3
  • 12
    • 84955504842 scopus 로고    scopus 로고
    • Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
    • M. Gan and C. Wang, "Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, " Mech. Syst. Signal Process., vol. 72, pp. 92-104, 2016.
    • (2016) Mech. Syst. Signal Process. , vol.72 , pp. 92-104
    • Gan, M.1    Wang, C.2
  • 13
    • 85020626243 scopus 로고    scopus 로고
    • Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine
    • Jun.
    • R. Liu, G. Meng, B. Yang, and X. F. Chen, "Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine, " IEEE Trans. Ind. Inform., vol. 13, no. 3, pp. 1310-1320, Jun. 2017.
    • (2017) IEEE Trans. Ind. Inform. , vol.13 , Issue.3 , pp. 1310-1320
    • Liu, R.1    Meng, G.2    Yang, B.3    Chen, X.F.4
  • 14
    • 85020653384 scopus 로고    scopus 로고
    • Convolutional discriminative feature learning for induction motor fault diagnosis
    • Jun.
    • W. Sun, R. Zhao, and R. Q. Yan., "Convolutional discriminative feature learning for induction motor fault diagnosis, " IEEE Trans. Ind. Informat., vol. 13, no. 3, pp. 1350-1359, Jun. 2017.
    • (2017) IEEE Trans. Ind. Informat. , vol.13 , Issue.3 , pp. 1350-1359
    • Sun, W.1    Zhao, R.2    Yan, R.Q.3
  • 15
    • 84955693855 scopus 로고    scopus 로고
    • Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
    • F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, " Mech. Syst. Signal Process., vol. 72, pp. 303-315, 2016.
    • (2016) Mech. Syst. Signal Process. , vol.72 , pp. 303-315
    • Jia, F.1    Lei, Y.2    Lin, J.3    Zhou, X.4    Lu, N.5
  • 16
    • 84964855691 scopus 로고    scopus 로고
    • A sparse auto-encoder-based deep neural network approach for induction motor faults classification
    • W. Sun, S. Shao, R. Zhao, and R. Q. Yan, "A sparse auto-encoder-based deep neural network approach for induction motor faults classification, " Measurement, vol. 89, pp. 171-178, 2016.
    • (2016) Measurement , vol.89 , pp. 171-178
    • Sun, W.1    Shao, S.2    Zhao, R.3    Yan, R.Q.4
  • 17
    • 85028524352 scopus 로고    scopus 로고
    • Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network
    • Mar.
    • H. Shao, H. Jiang, and H. Zhang, "Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network, " IEEE Trans. Ind. Electron., vol. 65, no. 3, pp. 2727-2736, Mar. 2018.
    • (2018) IEEE Trans. Ind. Electron. , vol.65 , Issue.3 , pp. 2727-2736
    • Shao, H.1    Jiang, H.2    Zhang, H.3
  • 18
    • 84875848937 scopus 로고    scopus 로고
    • Failure diagnosis using deep belief learning based health state classification
    • P. Tamilselvan and P. Wang, "Failure diagnosis using deep belief learning based health state classification, " Reliab. Eng. Syst. Saf., vol. 115, pp. 124-135, 2013.
    • (2013) Reliab. Eng. Syst. Saf. , vol.115 , pp. 124-135
    • Tamilselvan, P.1    Wang, P.2
  • 19
    • 85028452247 scopus 로고    scopus 로고
    • Discriminative deep belief networks with ant colony optimization for health status assessment of machine
    • Dec.
    • M. Ma, C. Sun, and X. Chen, "Discriminative deep belief networks with ant colony optimization for health status assessment of machine, " IEEE Trans. Instrum. Meas., vol. 66, no. 12, pp. 3115-3125, Dec. 2017.
    • (2017) IEEE Trans. Instrum. Meas. , vol.66 , Issue.12 , pp. 3115-3125
    • Ma, M.1    Sun, C.2    Chen, X.3
  • 20
    • 85008219650 scopus 로고    scopus 로고
    • An enhancement deep feature fusion method for rotating machinery fault diagnosis
    • H. Shao, H. Jiang, and F. Wang, "An enhancement deep feature fusion method for rotating machinery fault diagnosis, " Knowl.-Based Syst., vol. 119, pp. 200-220, 2017.
    • (2017) Knowl.-Based Syst. , vol.119 , pp. 200-220
    • Shao, H.1    Jiang, H.2    Wang, F.3
  • 21
    • 85016125628 scopus 로고    scopus 로고
    • Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network
    • Z. Chen andW. Li, "Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network, " IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1693-1702, 2017.
    • (2017) IEEE Trans. Instrum. Meas. , vol.66 , Issue.7 , pp. 1693-1702
    • Chen, Z.1    Li, W.2
  • 22
    • 85041212822 scopus 로고    scopus 로고
    • Deep coupling autoencoder for fault diagnosis with multimodal sensory data
    • Mar.
    • M. Ma, C. Sun, and X. Chen, "Deep coupling autoencoder for fault diagnosis with multimodal sensory data, " IEEE Trans. Ind. Informat., vol. 14, no. 3, pp. 1137-1145, Mar. 2018.
    • (2018) IEEE Trans. Ind. Informat. , vol.14 , Issue.3 , pp. 1137-1145
    • Ma, M.1    Sun, C.2    Chen, X.3
  • 24
    • 84903724014 scopus 로고    scopus 로고
    • Deep learning: Methods and applications
    • L. Deng and D. Yu, "Deep learning: Methods and applications, " Found. Trends Signal Process., vol. 7, no. 3-4, pp. 197-387, 2013.
    • (2013) Found. Trends Signal Process. , vol.7 , Issue.3-4 , pp. 197-387
    • Deng, L.1    Yu, D.2
  • 25
    • 84865768819 scopus 로고    scopus 로고
    • Deep convex networks: A scalable architecture for speech pattern classification
    • L. Deng and D. Yu, "Deep convex networks: A scalable architecture for speech pattern classification, " in Proc. 12th Annu. Conf. Int. Speech Commun. Assoc., 2011, pp. 2285-2288.
    • (2011) Proc. 12th Annu. Conf. Int. Speech Commun. Assoc. , pp. 2285-2288
    • Deng, L.1    Yu, D.2
  • 27
    • 33846798101 scopus 로고    scopus 로고
    • Subspace-based gearbox condition monitoring by kernel principal component analysis
    • Q. He, F. Kong, and R Yan, "Subspace-based gearbox condition monitoring by kernel principal component analysis, " Mech. Syst. Signal Process., vol. 21, no. 4, pp. 1755-1772, 2007.
    • (2007) Mech. Syst. Signal Process. , vol.21 , Issue.4 , pp. 1755-1772
    • He, Q.1    Kong, F.2    Yan, R.3
  • 28
    • 84867865623 scopus 로고    scopus 로고
    • Kernel ridge regression with lagged-dependent variable: Applications to prediction of internal bond strength in a medium density fiberboard process
    • Nov.
    • N. Kim, Y. S. Jeong, and M. K. Jeong, "Kernel ridge regression with lagged-dependent variable: Applications to prediction of internal bond strength in a medium density fiberboard process, " IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 42, no. 6, pp. 1011-1020, Nov. 2012.
    • (2012) IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. , vol.42 , Issue.6 , pp. 1011-1020
    • Kim, N.1    Jeong, Y.S.2    Jeong, M.K.3
  • 30
    • 84946567189 scopus 로고    scopus 로고
    • Accessed Mar. 2018
    • Bearing data set. [Online]. Available: http://csegroups. case. edu/ bearingdatacenter. Accessed Mar. 2018.
    • Bearing Data Set
  • 31
    • 85027957439 scopus 로고    scopus 로고
    • A nonprobabilistic metric derived from condition information for operational reliability assessment of aero-engines
    • Mar.
    • C. Sun, Z. He, H. Cao, Z. Zhang, X. Chen, and M. J. Zuo, "A nonprobabilistic metric derived from condition information for operational reliability assessment of aero-engines, " IEEE Trans. Reliab., vol. 64, no. 1, pp. 167-181, Mar. 2015.
    • (2015) IEEE Trans. Reliab. , vol.64 , Issue.1 , pp. 167-181
    • Sun, C.1    He, Z.2    Cao, H.3    Zhang, Z.4    Chen, X.5    Zuo, M.J.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.