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Bibliography [1] The lydia project. http://www.st.ewi.tudelft.nl/˜gemund/Lydia. [2] The servicewax project. Philips Medical Systems Nederland B.V. (Intranet), 2005-2006. Nonpublic data warehouse. [3] Peter Ashenden. The vhdl cookbook. ftp://ftp.cs.adelaide.edu.au/pub/ VHDL-Cookbook/. [4] Algirdas Avizienis, Jean-Claude Laprie, Brian Randell, and Carl Landwehr. Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on dependable and secure computing, 1(1), January-March 2004. [5] A. Barve. Model-based diagnosis: an asml case study. Master’s thesis, Delft University of Technology, August 2005. [6] M. Berkel. Field monitoring cardio vasculair. Technical report, Fontys Hogescholen / Philips Medical Systems Nederland B.V., June 2005. [7] Mike Y. Chen, Emre Kiciman, Eugene Fratkin, Armando Fox, and Eric Brewer. Pinpoint: Problem determination in large, dynamic internet services. In International Conference on Dependable Systems and Networks (Bethesda, MD), pages 595–604. IEEE Computer, Society, 2002. [8] L. Console, D.T. Dupr, and P.Torasso. On the relationship between abduction and deduction. J. Logic and Computation, 1(5):661–690, October 1991. [9] Sourabh Dash and Venkat Venkatasubramanian. Challenges in the industrial applications of fault diagnostic systems. Technical report, Purdue University, Laboratory of Intelligent Process Systems, 2000. [10] J. de Kleer, A.K. Mackworth, and R. Reiter. Characterizing diagnoses and systems. Artificial Intelligence, 56:197–222, 1992. [11] Johan de Kleer and Brian C. Williams. Diagnosing multiple faults. In Readings in Nonmonotic Reasoning (Mathew L. Ginsberg, ed.), pages 372–388. Los Altos, California: Morgan Kaufman, 1987. 73
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Automated Fault Diagnosis at Philip
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© 2006 W.M. Lindhoud. Document gen
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Preface The work presented in this
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CONTENTS 4.4 Modeling . . . . . . .
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List of Tables 3.1 Evaluation appro
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1.1 Fault Diagnosis Introduction Fi
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1.2 Automated Fault Diagnosis Intro
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1.5 Outline of the Thesis Introduct
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Chapter 2 State-of-the-Practice Fau
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State-of-the-Practice Fault diagnos
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Chapter 3 Automated Fault Diagnosis
Page 35 and 36: Automated Fault Diagnosis 3.1 Overv
Page 41 and 42: Automated Fault Diagnosis 3.3 Off-l
Page 43 and 44: Automated Fault Diagnosis 3.5 Model
Page 45: Automated Fault Diagnosis 3.6 Evalu
Page 48 and 49: 4.1 Fundamentals Model-Based Fault
Page 50 and 51: 4.2 Model-Based Diagnosis with LYDI
Page 52 and 53: 4.3 Basic Assumptions Model-Based F
Page 54 and 55: 4.4 Modeling Model-Based Fault Diag
Page 56 and 57: 4.4 Modeling Model-Based Fault Diag
Page 58 and 59: 4.5 Entropy Gain Model-Based Fault
Page 60 and 61: 4.6 MBD on the Power Supply Model-B
Page 63 and 64: Chapter 5 Diagnosing the Beam Prope
Page 65 and 66: Diagnosing the Beam Propeller Movem
Page 83 and 84: Chapter 6 Conclusions and Recommend
Page 85: Conclusions and Recommendations 6.2
Page 89: BIBLIOGRAPHY [29] B. Water. Field m
Page 92 and 93: Glossary of terms • Cardio-Vascul
Page 94 and 95: Glossary of terms • Fault Scenari
Page 96 and 97: Glossary of terms • Recovery: see
Page 99 and 100: Appendix B Case Study Details In th
Page 101 and 102: Case Study Details B.4 Full List of
Page 103 and 104: Case Study Details B.5 Discretizati
Page 105 and 106: Appendix C Lydia Models This append
Page 107 and 108: Lydia Models C.1 The Synthetic Mode
Page 109 and 110: Lydia Models C.2 Model of the Power
Page 115 and 116: Lydia Models C.3 The Models Constru
Page 129: Lydia Models C.3 The Models Constru
Page 132: State-of-the-Future Fault Diagnosis
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