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© 2006 W.M. Lindhoud. Document generated with LATEX, MiKTeX and WinEdt. Cover picture: Philips Cardio-Vascular X-Ray System, © Philips
Automated Fault Diagnosis at Philips Medical Systems A Model-Based Approach Author: W.M. Lindhoud Student id: 1053884 Email: w.m.lindhoud@gmail.com Abstract While our machines get faster, better and cheaper every day the increase in complexity of these systems is huge. This is not different for the medical systems developed and serviced by Philips Medical Systems (PMS). Fault diagnosis is an essential key to keep these systems dependable. Currently, most fault diagnosis practices in industry are based on manual effort. An area that is not readily explored and exploited by industry, but could offer improvement, is automated fault diagnosis. Although, many useful mechanisms inside the PMS systems exist, so far, no research has been done on how to set up a diagnostic system. This work is a first exploration of the benefits that such a technique could have for the fault diagnosis of the Philips Cardio-Vascular X-Ray System. In particular, it defines the goals and qualities of a diagnostic approach in industry. Model-Based fault Diagnosis (MBD) is a reasoning technique for finding root causes of failures based upon a model. MBD suits the goals and desired attributes for a fault diagnosis approach at PMS best. A case study of a subsystem is used to examine what issues play a role when implementing a model-based approach to fault diagnosis. It is found that, at PMS, it is not possible to quantify the diagnostic accuracy of the current practice to fault diagnosis and MBD implementations. Consequently, it cannot be concluded that MBD improves fault diagnosis with respect to higher dependability of Philips Cardio-Vascular X-Ray Systems. However, by means of entropy it is shown that the accuracy of the model-based approach is expected to be higher than an approach in which experts define a mapping between symptoms and diagnoses. Further, it is shown that entropy is a valuable feedback mechanism to improve MBD implementations. keywords: Model-Based Diagnosis, Fault Diagnosis, Service Thesis Committee: Chair: University supervisor: Company supervisor: Committee Member: prof. dr. ir. A.J.C. van Gemund, Faculty EEMCS, TU Delft dr. ir. H.G. Gross, Faculty EEMCS, TU Delft ing. B. Minderhoud, Philips Medical Systems dr. M.M. de Weerdt, Faculty EEMCS, TU Delft
Page 1: Automated Fault Diagnosis at Philip
Page 7: Preface The work presented in this
Page 10 and 11: CONTENTS 4.4 Modeling . . . . . . .
Page 13: List of Tables 3.1 Evaluation appro
Page 16 and 17: 1.1 Fault Diagnosis Introduction Fi
Page 18 and 19: 1.2 Automated Fault Diagnosis Intro
Page 20 and 21: 1.5 Outline of the Thesis Introduct
Page 23 and 24: Chapter 2 State-of-the-Practice Fau
Page 25 and 26: State-of-the-Practice Fault diagnos
Page 33 and 34: 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
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4.4 Modeling Model-Based Fault Diag
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4.4 Modeling Model-Based Fault Diag
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4.5 Entropy Gain Model-Based Fault
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4.6 MBD on the Power Supply Model-B
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Chapter 5 Diagnosing the Beam Prope
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Diagnosing the Beam Propeller Movem
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Chapter 6 Conclusions and Recommend
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Conclusions and Recommendations 6.2
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BIBLIOGRAPHY [12] Johan de Kleer an
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Appendix A Glossary of terms In thi
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Glossary of terms • Diagnostic Pe
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Glossary of terms • Model: an abs
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Glossary of terms • System: an en
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B.2 System Data Case Study Details
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B.4 Full List of Examined Fault Sce
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B.5 Discretization of Implementatio
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C.1 The Synthetic Models that are U
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C.2 Model of the Power Supply Examp
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C.3 The Models Constructed for the
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Appendix D State-of-the-Future Faul
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