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Volumn 35, Issue , 2014, Pages 117-123

An adaptive method for health trend prediction of rotating bearings

Author keywords

Adaptive prediction; Confidence value; Empirical mode decomposition; Rotating bearing degradation; Self organizing map

Indexed keywords

CONFORMAL MAPPING; DEGRADATION; HEALTH; SELF ORGANIZING MAPS; SIGNAL PROCESSING; VIBRATION ANALYSIS;

EID: 85027917035     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2014.08.006     Document Type: Article
Times cited : (116)

References (38)
  • 1
    • 33646534620 scopus 로고    scopus 로고
    • A review on machinery diagnostics and prognostics implementing condition-based maintenance
    • A.K.S. Jardine, D. Lin, and D. Banjevic A review on machinery diagnostics and prognostics implementing condition-based maintenance Mech. Syst. Signal Process. 20 7 2006 1483 1510
    • (2006) Mech. Syst. Signal Process. , vol.20 , Issue.7 , pp. 1483-1510
    • Jardine, A.K.S.1    Lin, D.2    Banjevic, D.3
  • 2
    • 84858080502 scopus 로고    scopus 로고
    • A rotating machinery fault diagnosis method based on local mean decomposition
    • J. Cheng, Y. Yang, and Y. Yang A rotating machinery fault diagnosis method based on local mean decomposition Digit. Signal Process. 22 2012 356 366
    • (2012) Digit. Signal Process. , vol.22 , pp. 356-366
    • Cheng, J.1    Yang, Y.2    Yang, Y.3
  • 3
    • 70350712142 scopus 로고    scopus 로고
    • Nonstationary approaches to trend identification and denoising of measured power system oscillations
    • A.R. Messina, V. Vittal, G.T. Heydt, and T.J. Browne Nonstationary approaches to trend identification and denoising of measured power system oscillations IEEE Trans. Power Syst. 24 2009 1798 1807
    • (2009) IEEE Trans. Power Syst. , vol.24 , pp. 1798-1807
    • Messina, A.R.1    Vittal, V.2    Heydt, G.T.3    Browne, T.J.4
  • 4
    • 70350764824 scopus 로고    scopus 로고
    • Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means
    • Y. Pan, J. Chen, and X. Li Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means Mech. Syst. Signal Process. 24 2010 559 566
    • (2010) Mech. Syst. Signal Process. , vol.24 , pp. 559-566
    • Pan, Y.1    Chen, J.2    Li, X.3
  • 5
    • 84860375483 scopus 로고    scopus 로고
    • Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
    • C. Hu, B.D. Youn, P. Wang, and J. Taek Yoon Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life Reliab. Eng. Syst. Saf. 103 2012 120 135
    • (2012) Reliab. Eng. Syst. Saf. , vol.103 , pp. 120-135
    • Hu, C.1    Youn, B.D.2    Wang, P.3    Taek Yoon, J.4
  • 6
    • 82255174981 scopus 로고    scopus 로고
    • Early fault diagnosis of rotating machinery based on wavelet packets - Empirical mode decomposition feature extraction and neural network
    • G.F. Bin, J.J. Gao, X.J. Li, and B.S. Dhillon Early fault diagnosis of rotating machinery based on wavelet packets - empirical mode decomposition feature extraction and neural network Mech. Syst. Signal Process. 27 2012 696 711
    • (2012) Mech. Syst. Signal Process. , vol.27 , pp. 696-711
    • Bin, G.F.1    Gao, J.J.2    Li, X.J.3    Dhillon, B.S.4
  • 7
    • 84861603437 scopus 로고    scopus 로고
    • Combining Relevance Vector Machines and exponential regression for bearing residual life estimation
    • F. Di Maio, K.L. Tsui, and E. Zio Combining Relevance Vector Machines and exponential regression for bearing residual life estimation Mech. Syst. Signal Process. 31 2012 405 427
    • (2012) Mech. Syst. Signal Process. , vol.31 , pp. 405-427
    • Di Maio, F.1    Tsui, K.L.2    Zio, E.3
  • 8
    • 77958041293 scopus 로고    scopus 로고
    • Current status of machine prognostics in condition-based maintenance: A review
    • Y. Peng, M. Dong, and M.J. Zuo Current status of machine prognostics in condition-based maintenance: a review Int. J. Adv. Manuf. Technol. 50 1-4 2010 297 313
    • (2010) Int. J. Adv. Manuf. Technol. , vol.50 , Issue.14 , pp. 297-313
    • Peng, Y.1    Dong, M.2    Zuo, M.J.3
  • 9
    • 58049190180 scopus 로고    scopus 로고
    • Rotating machinery prognostics: State of the art, challenges and opportunities
    • A. Heng, S. Zhang, A.C. Tan, and J. Mathew Rotating machinery prognostics: state of the art, challenges and opportunities Mech. Syst. Signal Process. 23 2009 724 739
    • (2009) Mech. Syst. Signal Process. , vol.23 , pp. 724-739
    • Heng, A.1    Zhang, S.2    Tan, A.C.3    Mathew, J.4
  • 10
    • 79955584653 scopus 로고    scopus 로고
    • Remaining useful life estimation - A review on the statistical data driven approaches
    • X. Si, W. Wang, C. Hu, and D. Zhou Remaining useful life estimation - a review on the statistical data driven approaches Eur. J. Oper. Res. 213 2011 1 14
    • (2011) Eur. J. Oper. Res. , vol.213 , pp. 1-14
    • Si, X.1    Wang, W.2    Hu, C.3    Zhou, D.4
  • 11
    • 84878107122 scopus 로고    scopus 로고
    • Remaining useful life estimation based on nonlinear feature reduction and support vector regression
    • T. Benkedjouh, K. Medjaher, N. Zerhouni, and S. Rechak Remaining useful life estimation based on nonlinear feature reduction and support vector regression Eng. Appl. Artif. Intell. 26 2013 1751 1760
    • (2013) Eng. Appl. Artif. Intell. , vol.26 , pp. 1751-1760
    • Benkedjouh, T.1    Medjaher, K.2    Zerhouni, N.3    Rechak, S.4
  • 12
    • 84879322256 scopus 로고    scopus 로고
    • A dynamic particle filter-support vector regression method for reliability prediction
    • Z. Wei, T. Tao, D. ZhuoShu, and E. Zio A dynamic particle filter-support vector regression method for reliability prediction Reliab. Eng. Syst. Saf. 119 2013 109 116
    • (2013) Reliab. Eng. Syst. Saf. , vol.119 , pp. 109-116
    • Wei, Z.1    Tao, T.2    Zhuoshu, D.3    Zio, E.4
  • 13
    • 79952455682 scopus 로고    scopus 로고
    • Machine health prognostics using survival probability and support vector machine
    • A. Widodo, and B.S. Yang Machine health prognostics using survival probability and support vector machine Expert Syst. Appl. 38 7 2011 8430 8437
    • (2011) Expert Syst. Appl. , vol.38 , Issue.7 , pp. 8430-8437
    • Widodo, A.1    Yang, B.S.2
  • 14
    • 84879103867 scopus 로고    scopus 로고
    • Multiple optimized online support vector regression for adaptive time series prediction
    • D. Liu, Y. Peng, J. Li, and X. Peng Multiple optimized online support vector regression for adaptive time series prediction Measurement 46 8 2013 2391 2404
    • (2013) Measurement , vol.46 , Issue.8 , pp. 2391-2404
    • Liu, D.1    Peng, Y.2    Li, J.3    Peng, X.4
  • 15
    • 84878682978 scopus 로고    scopus 로고
    • A framework for predicting the remaining useful life of a single unit under time-varying operating conditions
    • H. Liao, and Z. Tian A framework for predicting the remaining useful life of a single unit under time-varying operating conditions IIE Trans. 45 9 2013 964 980
    • (2013) IIE Trans. , vol.45 , Issue.9 , pp. 964-980
    • Liao, H.1    Tian, Z.2
  • 16
    • 84896314154 scopus 로고    scopus 로고
    • Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction
    • L. Liao, and F. Kottig Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction IEEE Trans. Reliab. 63 1 2014 191 207
    • (2014) IEEE Trans. Reliab. , vol.63 , Issue.1 , pp. 191-207
    • Liao, L.1    Kottig, F.2
  • 17
    • 84895057276 scopus 로고    scopus 로고
    • Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model
    • J. Sun, H. Zuo, and W. Wang Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model Mech. Syst. Signal Process. 45 2 2014 396 407
    • (2014) Mech. Syst. Signal Process. , vol.45 , Issue.2 , pp. 396-407
    • Sun, J.1    Zuo, H.2    Wang, W.3
  • 18
    • 84891769069 scopus 로고    scopus 로고
    • Prognosis of bearing failures using hidden Markov models and the adaptive neuro-fuzzy inference system
    • A. Soualhi, H. Razik, and G. Clerc Prognosis of bearing failures using hidden Markov models and the adaptive neuro-fuzzy inference system IEEE Trans. Ind. Electron. 61 6 2014 2864 2874
    • (2014) IEEE Trans. Ind. Electron. , vol.61 , Issue.6 , pp. 2864-2874
    • Soualhi, A.1    Razik, H.2    Clerc, G.3
  • 19
    • 85028125106 scopus 로고    scopus 로고
    • Framework of designing an adaptive and multi-regime prognostics and health management for wind turbine reliability and efficiency improvement
    • B.L. Song, and J. Lee Framework of designing an adaptive and multi-regime prognostics and health management for wind turbine reliability and efficiency improvement Int. J. Adv. Comput. Sci. Appl. 4 2 2013 142 149
    • (2013) Int. J. Adv. Comput. Sci. Appl. , vol.4 , Issue.2 , pp. 142-149
    • Song, B.L.1    Lee, J.2
  • 20
    • 84887056149 scopus 로고    scopus 로고
    • Prognostics and health management design for rotary machinery systems - Reviews, methodology and applications
    • J. Lee, F. Wu, and W. Zhao Prognostics and health management design for rotary machinery systems - reviews, methodology and applications Mech. Syst. Signal Process. 42 1 2014 314 334
    • (2014) Mech. Syst. Signal Process. , vol.42 , Issue.1 , pp. 314-334
    • Lee, J.1    Wu, F.2    Zhao, W.3
  • 21
    • 84872343379 scopus 로고    scopus 로고
    • Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data
    • P. Baraldi, F. Mangili, and E. Zio Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data Reliab. Eng. Syst. Saf. 112 2012 94 108
    • (2012) Reliab. Eng. Syst. Saf. , vol.112 , pp. 94-108
    • Baraldi, P.1    Mangili, F.2    Zio, E.3
  • 22
    • 78049451757 scopus 로고    scopus 로고
    • An online adaptive condition-based maintenance method for mechanical systems
    • F. Wu, T. Wang, and J. Lee An online adaptive condition-based maintenance method for mechanical systems Mech. Syst. Signal Process. 24 8 2010 2985 2995
    • (2010) Mech. Syst. Signal Process. , vol.24 , Issue.8 , pp. 2985-2995
    • Wu, F.1    Wang, T.2    Lee, J.3
  • 24
    • 70349443232 scopus 로고    scopus 로고
    • Design of a reconfigurable prognostics platform for machine tools
    • L. Liao, and J. Lee Design of a reconfigurable prognostics platform for machine tools Expert Syst. Appl. 37 1 2010 240 252
    • (2010) Expert Syst. Appl. , vol.37 , Issue.1 , pp. 240-252
    • Liao, L.1    Lee, J.2
  • 26
    • 81055147063 scopus 로고    scopus 로고
    • The research based on empirical mode decomposition in bearing fault diagnosis
    • T.L. Xu, X.Z. Lang, X.Y. Zhang, and X.C. Pei The research based on empirical mode decomposition in bearing fault diagnosis Appl. Mech. Mater. 103 2012 225 228
    • (2012) Appl. Mech. Mater. , vol.103 , pp. 225-228
    • Xu, T.L.1    Lang, X.Z.2    Zhang, X.Y.3    Pei, X.C.4
  • 27
    • 84870404381 scopus 로고    scopus 로고
    • A review on empirical mode decomposition in fault diagnosis of rotating machinery
    • Y. Lei, J. Lin, Z. He, and M.J. Zuo A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech. Syst. Signal Process. 35 2013 108 126
    • (2013) Mech. Syst. Signal Process. , vol.35 , pp. 108-126
    • Lei, Y.1    Lin, J.2    He, Z.3    Zuo, M.J.4
  • 28
    • 5044252073 scopus 로고    scopus 로고
    • Robust performance degradation assessment methods for enhanced rolling element bearing prognostics
    • H. Qiu, J. Lee, J. Lin, and G. Yu Robust performance degradation assessment methods for enhanced rolling element bearing prognostics Adv. Eng. Inform. 17 2003 127 140
    • (2003) Adv. Eng. Inform. , vol.17 , pp. 127-140
    • Qiu, H.1    Lee, J.2    Lin, J.3    Yu, G.4
  • 29
    • 84875254721 scopus 로고    scopus 로고
    • Dynamic degradation observer for bearing fault by MTS-SOM system
    • J. Hu, L. Zhang, and W. Liang Dynamic degradation observer for bearing fault by MTS-SOM system Mech. Syst. Signal Process. 36 2 2012 385 400
    • (2012) Mech. Syst. Signal Process. , vol.36 , Issue.2 , pp. 385-400
    • Hu, J.1    Zhang, L.2    Liang, W.3
  • 30
    • 79955827452 scopus 로고    scopus 로고
    • A hybrid feature selection scheme and self-organizing map model for machine health assessment
    • J. Yu A hybrid feature selection scheme and self-organizing map model for machine health assessment Appl. Soft Comput. 11 2011 4041 4054
    • (2011) Appl. Soft Comput. , vol.11 , pp. 4041-4054
    • Yu, J.1
  • 31
    • 0035439321 scopus 로고    scopus 로고
    • Fault prognostics using dynamic wavelet neural networks
    • P. Wang, and G. Vachtsevanos Fault prognostics using dynamic wavelet neural networks Artif. Intell. Eng. Des. Anal. Manuf. 15 4 2001 349 365
    • (2001) Artif. Intell. Eng. Des. Anal. Manuf. , vol.15 , Issue.4 , pp. 349-365
    • Wang, P.1    Vachtsevanos, G.2
  • 32
    • 79951581707 scopus 로고    scopus 로고
    • EEMD method and WNN for fault diagnosis of locomotive roller bearings
    • Y. Lei, Z. He, and Y. Zi EEMD method and WNN for fault diagnosis of locomotive roller bearings Expert Syst. Appl. 38 6 2011 7334 7341
    • (2011) Expert Syst. Appl. , vol.38 , Issue.6 , pp. 7334-7341
    • Lei, Y.1    He, Z.2    Zi, Y.3
  • 33
    • 77952791421 scopus 로고    scopus 로고
    • Distributed prognostic health management with Gaussian process regression
    • S. Saha, B. Saha, A. Saxena, and K. Goebel Distributed prognostic health management with Gaussian process regression IEEE Aerospace Conference 2010 1 8
    • (2010) IEEE Aerospace Conference , pp. 1-8
    • Saha, S.1    Saha, B.2    Saxena, A.3    Goebel, K.4
  • 34
    • 70350714884 scopus 로고    scopus 로고
    • Intelligent analysis model of slope nonlinear displacement time series based on genetic-Gaussian process regression algorithm of combined kernel function
    • K. Liu, B. Liu, and C. Xu Intelligent analysis model of slope nonlinear displacement time series based on genetic-Gaussian process regression algorithm of combined kernel function Chin. J. Rock Mech. Eng. 10 2009 2128 2134
    • (2009) Chin. J. Rock Mech. Eng. , vol.10 , pp. 2128-2134
    • Liu, K.1    Liu, B.2    Xu, C.3


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