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Volumn 1, Issue , 2010, Pages

Fault detection and identification in NPP instruments using kernel principal component analysis

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DETECTION AND IDENTIFICATION; FAULT ISOLATION; KERNEL PRINCIPAL COMPONENT ANALYSIS; MEAN VALUES; RECONSTRUCTION ERROR;

EID: 80053266496     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/ICONE18-29777     Document Type: Conference Paper
Times cited : (9)

References (17)
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  • 7
    • 0026113980 scopus 로고
    • Nonlinear principal component analysis using autoassociative neural networks
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    • Kramer, M.A.1
  • 8
    • 0043015539 scopus 로고    scopus 로고
    • Nonlinear principal component analysis-based on principal curves and neural networks
    • Dong, D. and McAvoy, T. J. "Nonlinear Principal Component Analysis-Based on principal Curves and Neural Networks ." Computers & Chemical Engineering 30 (1996): 65-78.
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    • Nonlinear component analysis as a kernel eigenvalue problem
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    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 10
    • 84898970836 scopus 로고    scopus 로고
    • Kernel PCA and De-noising in feature spaces
    • eds: Kearns, M. S., Solla, S. A. and Cohn, D. A. Cambridge, MA: MIT Press
    • Mika, S., et al. "Kernel PCA and De-Noising in Feature Spaces." Advances in Neural Information Processing Systems 11. eds: Kearns, M. S., Solla, S. A. and Cohn, D. A. Cambridge, MA: MIT Press, 1999. 536-542.
    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 536-542
    • Mika, S.1
  • 11
    • 0346911568 scopus 로고    scopus 로고
    • Nonlinear process monitoring using kernel principal component analysis
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    • Fault detection and identification of nonlinear processes based on Kernel PCA
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    • Identification of faulty sensors using principal component analysis
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.