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Volumn 33, Issue 1-2, 2008, Pages 185-193

Facility health maintenance through SVR-driven degradation prediction

Author keywords

Degradation trend; Facility synthetic failure probability model; Health monitoring and maintenance; Logistic regression; Support vector regression

Indexed keywords

HEALTH; MAINTENANCE; MONTE CARLO METHODS; REGRESSION ANALYSIS;

EID: 49149125163     PISSN: 02681900     EISSN: None     Source Type: Journal    
DOI: 10.1504/IJMPT.2008.019781     Document Type: Article
Times cited : (10)

References (12)
  • 1
    • 0032324903 scopus 로고    scopus 로고
    • A predicting and preventing machine failures
    • Becker, K.C., Forbes, N.A. and Forbes, N. (1998) 'A predicting and preventing machine failures', Industrial Physicists, Vol. 4, No. 4, pp.20-23.
    • (1998) Industrial Physicists , vol.4 , Issue.4 , pp. 20-23
    • Becker, K.C.1    Forbes, N.A.2    Forbes, N.3
  • 8
    • 0032594959 scopus 로고    scopus 로고
    • An overview of statistical learning theory
    • Vapnik, V.N. (1999) 'An overview of statistical learning theory', IEEE Trans. Neural Networks. Vol. 10, pp.988-999.
    • (1999) IEEE Trans. Neural Networks , vol.10 , pp. 988-999
    • Vapnik, V.N.1
  • 12
    • 0001301007 scopus 로고    scopus 로고
    • Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery
    • Zhang, S. and Ganesan, R. (1997) 'Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery', Journal of Engineering for Gas Turbines and Power, Vol. 119, pp.378-384.
    • (1997) Journal of Engineering for Gas Turbines and Power , vol.119 , pp. 378-384
    • Zhang, S.1    Ganesan, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.