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Volumn 28, Issue 1, 2013, Pages 142-148

Dynamic data window fault detection method based on relative principal component analysis

Author keywords

Dynamic data window; Fault detect; RPCA; T2 statistic

Indexed keywords

CONTROL LIMITS; DYNAMIC DATA WINDOWS; FALSE ALARMS; FAULT DETECT; PRINCIPAL COMPONENTS; RPCA; WIND POWER GENERATION SYSTEMS;

EID: 84874734456     PISSN: 10006753     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (9)

References (11)
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  • 2
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    • Statistical process control tools for a semiconductor each process: Improving robustness through model updating
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    • (1997) Proceeding of the ADCHEM , pp. 78-83
    • Gallagher, N.B.1    Bulter, W.B.M.2    White, S.W.3
  • 3
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    • Exponentially weighted moving principal component analysis and projection to latent stuctures. chemometrics and intelligent laboratory systems
    • Wold S. Exponentially weighted moving principal component analysis and projection to latent stuctures. chemometrics and intelligent laboratory systems[C]. Proceedings of the 3rd Scandinavian Symposium on Chemometrics, 1994: 149-161.
    • (1994) Proceedings of the 3rd Scandinavian Symposium on Chemometrics , pp. 149-161
    • Wold, S.1
  • 4
    • 0034301495 scopus 로고    scopus 로고
    • Recursive PCA for adaptive process monitoring
    • Li Weihua, Henry Yue H, Sergio Valle Cervantes, et al. Recursive PCA for adaptive process monitoring[J]. Journal of Process Monitoring, 2000, 10(5): 471-486.
    • (2000) Journal of Process Monitoring , vol.10 , Issue.5 , pp. 471-486
    • Li, W.1    Henry Yue, H.2    Cervantes, S.V.3
  • 5
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    • Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process
    • Lane S, Martin E B, Morrios A J et al. Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process[J]. Transactions of the Institute of Measurement and Control, 2003, 25(1): 17-35.
    • (2003) Transactions of the Institute of Measurement and Control , vol.25 , Issue.1 , pp. 17-35
    • Lane, S.1    Martin, E.B.2    Morrios, A.J.3
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  • 9
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    • Relative principal component analysis algorithm and its application in fault detection
    • Wang Tianzhen, Tang Tianhao, Wen Chenglin. Relative principal component analysis algorithm and its application in fault detection[J]. Journal of System Simulation, 2007, 19(13): 2889-2894.
    • (2007) Journal of System Simulation , vol.19 , Issue.13 , pp. 2889-2894
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  • 10
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    • Permanent magnet synchronous motor wind power system based on dual three-level converters
    • Liu Yi, Tan Guojun, Li Yuan. Permanent magnet synchronous motor wind power system based on dual three-level converters [J]. Electric Machine & Control Application, 2011, 38(4): 37-41.
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    • Study of fault diagnosis wind turbine generator systerm
    • Zhang Xinyan, He Shan, Zhang Xiaobo. Study of fault diagnosis wind turbine generator systerm[J]. Journal of Xinjiang University, 2009, 26(2): 140-144.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.