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잠재변수기법을 이용한 회전기계의 상태진단 및 예지보수 시스템 개발

이규호 2015년
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' 잠재변수기법을 이용한 회전기계의 상태진단 및 예지보수 시스템 개발' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 상태진단
  • 자기조직화 지도
  • 적응형인자 모델링
  • 주성분분석
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
336 0

0.0%

' 잠재변수기법을 이용한 회전기계의 상태진단 및 예지보수 시스템 개발' 의 참고문헌

  • 통계적 기법을 이용한 진공펌프 상태진단 시스템에 관한 연구
    성동원 석사학위논문 서울대하교 항공우주공학과 [2007]
  • Wold, S., N. Kettaneh, H. Friden and A. Holmberg, “modeling anddiagnostics of batch process and analogous kinetic pexperiments ” ,Technometrics, 44, pp331-340, (1998)
  • Wijk,J.J.V. and Liere, R.V.,”HyperSlice ? Visualization of scalar function of118many variables”, In Proceedings of the IEEE Visualization Conference, SanJose, California, 1993, pp119-125
  • W.S. Cheung, J.Y. Lim, K.H. Chung, S.G. Lee, U.S. Patent No. 7,664,618 (16Febuary 2010).
  • W. S. Cheung et. al., U.S. Patent No. 7653512 (2010).
  • V. Paravdova, B. Walczak, D.L. Massart, A comparison of two algorithms forwarping of analytical signals, Analytica Chimca acta, 456, 2002, pp 77-92
  • Theodora Kourti, Application of latent variable methods to process controland multivariate statistical process control in industry, Int. J. Adapt. ControlSignal Process, Vol.19, 2005, 213-246
  • Teuvo Kohonen, Self-Organizing Maps, Springer Series in Information Sciences, Vol.30,Springer, Berlin, Heidelberg, New York, 2001, 3rd edition.
  • Svense, M. ,“GTM: the genertative topographic mapping. ThD Thesis, AstonUniversity, Birmingham,1998
  • Shinkyu Jeong, Kazuhisa Chiba, Shigeru Obayashi “Data Mining forAerodynamic Design Space”, Journal of aerospace computing, information,and Space, Vol.2, 2005,pp.452-469
  • Shigeyasu Sako et al, e-diagnostics Technology for Supporting e-manufacturing,Hitachi Review Vol. 52 (2003), No. 3, pp171-175
  • S. Parashar, V. Pediroda and C. Poloni, “Self Organizing Maps for Designselection in Robust multi-objective design of aerofoil ” , 46th AIAAAerospace Sciences Meeting and Exhibit, Reno, Nevada, (2008),AIAA2008-914
  • Robert L. Mason, John C. Young, “Multivariate Statistical Process Controlwith Industrial Applications, ASA SIAM, Alexandria Virginia, 2002
  • Rasmus Bro, Age K. Smilde, “Centering and scaling in component analysis”,Journal of Chemometrics, vol.17, pp.16-33, (2003).
  • R. A. Becker and W. S. Cleveland, “Brushing Scatterplots”, Technometrics,vol.29, no.2, pp.127-142, 1987.
  • Paul Nomikos and John, F. MacGergor, “Multivariate SPC for monitoringbatch process”, Technometrics, 37(1), pp41-59, (1995).
  • Masoud Golshan, John F. MacGregor, Prashant Mhaskar, “Latent variablemodel predictive control for trajectory tracking in batch processes”, Journal ofProcess Control 20 (2010) 538~550
  • M.H.Habianian, ”Recommended Procedure for measuring pumping speeds”,J. Vac. Sci. Technol. A, Vol.5(4), 1987,pp2552-2557
  • Lindgren, F; Geladi, P; Wold, S (1993). "The kernel algorithm for PLS". J.Chemometrics 7: 45?59
  • Lapointe, F.J. and P. Legendre, “A classification of pure malt Scotchwhiskies, Applied Statistics, Vol.43, 1994, pp23-257
  • Kyuho, Lee et. al. “ Study on Vacuum Pump Monitoing Using AdaptiveParameter Modeling ” , Journal of the Korean Vacuum Society, 20(3),pp.165-175, (2011)
  • Koji Shimoyama, Kazuyuki Sugimura, Shinkyu Jeong, and Shigeu Obayashi,“Performace map construction for a centrifugal diffuser with data miningtechniques ” , Journal of Computational Science an Technology,Vol.4(1),(2010), pp3-5
  • Jolliffe I. T., Principal Component Analysis. New York, Berlin:Springer-Verlag, 1986117
  • Jackson, J.E., Mudholkar, G. S. Control procedures for residual associatedwith principal component analysis, Technometrics, 21, pp341-349, (1979).
  • J.Y. Lim, W.S. Cheung and K.H. Chung, Non-destructive characteristicsevaluation for low vacuum dry pump sin the semi-conductor manufacturingprocess line, Key Engineering Materials, 270-273, pp.2345-2350, 2004
  • ISO17359:2003(E), Condition Monitoring and Diagnostics of Machines -General Guidelines,(2003)
  • ISO17359-Condition Monitoring and diagnostics of Machines ? GeneralGuidelines
  • H. Chen, C. Schuffels, and R. Orwig. Internet categorization and search: aself-organizing approach. Journal of Visual Communication and Image119Representation, 7(1):88?102, 1996
  • Gnanadesikan, R. “Methods for Statistical Data Analysis of MultivariateObservations, Wiley, New York, 1977
  • D.W. Sung et. al. “Study on Vacumm pump monitoring using MPCAsatistical method”, Journal of the Korean Vacuum Society, 15, pp 338~346(2006).
  • D. Merkl and A. Rauber. Uncovering the hierarchical structure of text archivesby using an unsupervised neural network with adaptive architecture. InKnowledge Discovery and Data Mining, proceedings, pages 384?395, 2000.
  • D. Keim, M. Hao, U. Dayal, M. Hsu and J. Ladisch, “Pixel Bar Charts: A NewTechnique for Visualizing Large Multi-Attribute Data Sets withoutAggregation”, Proceedings of the IEEE Symposium on InformationVisualization 2001 (INFOVIS’01), pp.113-120, 2001.
  • D. G. Roussinov and H. Chen. A scalable self-organizing map algorithm fortextual classification: a neural network approach to thesaurus generation.CC-AI, The Journal for the Integrated Study of Artificial Intelligence,15(1?2):81?111, 1998.
  • B. Fritzke. Growing grid?a self-organizing network with constantneighbourhood range and adaptation strength. Neural Processing Letters,2(5):9?13, Sept 1995.
  • Alpern, B. and Carter, L.,” The Hyperbox”, In Proceedings of the IEEEVisualization Conference, San Diego, California, 1991, pp133-139
  • Aapo Hyvarinen, Juha Karhunen, "Independent Component Analysis",WILEY INTER-SCIENCE
  • A. Kassidas, J.F.MacGregor, P.Taylor, “Synchronization of batch trajectoriesusing dynamic time warping”, AIChE Journal, 44, pp. 864-875, (1998).
  • A. Inselberg, “Multidimensional Detective”, Proceedings of the IEEESymposium on Information Visualization, pp.100-107, 1997.