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5.7 Results of the Case Study Diagnosing the Beam Propeller Movement of the Frontal Stand S Observations D real D MBD−2 A S12 (0,0,0,1,0,0) ¬h_MBU ¬h_Stand 0 S13 (0,0,1,0,0,0) ¬h_PEU ¬h_PEU 1 S14 (0,0,1,0,0,0) ¬h_PEU ¬h_PEU 1 S15 (0,0,0,1,0,0) ¬h_Stand ¬h_Stand 1 S16 (0,0,0,0,0,1) ¬h_MBU ¬h_Stand 0 Table 5.13: 5 additional fault scenarios, of which adjudged broken components are known. Suppose this value is considered too low by the management of PMS. What can be done to increase it? This could be done by improving models, or extending observability. Entropy can be used to estimate the diagnostic performance of the fault scenarios. Even if no fault scenarios are available, entropy can be used to predict if the accuracy increases on future fault scenarios. The relation between entropy and accuracy is that solutions with lower entropy have equal or higher accuracy on the same set of fault scenarios [20]. 68
Chapter 6 Conclusions and Recommendations The complexity crisis is most visible in complex, multi-disciplinary embedded systems. Although individual components might work well separately, the system as a whole may exhibit unexpected faults. More and more components are bought third party and might not be used as the supplier expected when developing them. There is a complex dynamic interaction between subsystems, and this is what makes fault diagnosis difficult and time consuming. The Philips Cardio-Vascular X- Ray system is a complex, multi-disciplinary embedded system. The current efforts for doing fault diagnosis at PMS is increasing; the portion of the PMS employees working on service instead of development grows. This thesis presents possible improvement when using a model-based approach to fault diagnosis. 6.1 Conclusions In this thesis, it is shown that the current practice to fault diagnosis at PMS is not optimal with respect to the items that an ideal fault diagnosis technique would have; accuracy, speed of diagnosis, uncertainty of diagnosis, context independency, development costs, runtime costs, explanation facility, and adaptability. Furthermore, model-based diagnosis is chosen as the automated solution that could improve fault diagnosis, because this technique to automated fault diagnosis is likely to possess these items that, when present in fault diagnosis, improve dependability of the target system. Unfortunately, it is not possible to conclude that MBD improves fault diagnosis with respect to higher dependability of Philips Cardio-Vascular X-Ray Systems. In order to so, it must be possible to determine the accuracy of both the current practice to fault diagnosis, and the proposed technique MBD. This cannot be done. The reason for this is that it is impossible to find out what faults caused a certain failure. Although, in many cases, it is possible to determine the observations at the moment of failure (the error detection mechanisms in the Philips Cardio-Vascular X-Ray System produce clear log entries), it is impossible to determine the actual fault that caused the specific observable values. Without this information, the accuracy of the current practice, and a MBD implementation, cannot be quantified. In other words, the significant problem is the inability to determine what components recovered a certain failure. Entropy is a valuable metric to show the improvement of different MBD implementations. In the case study, the diagnostic performance of three MBD implementations that can diagnose the beam propeller of the frontal stand is estimated. The entropy gain of implementation MBD-2 compared to MBD-1 is zero, thus implementing MBD-2 is not useful. Adding extra sensors to the target system 69
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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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Page 60 and 61: 4.6 MBD on the Power Supply Model-B
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Page 88 and 89: BIBLIOGRAPHY [12] Johan de Kleer an
Page 91 and 92: Appendix A Glossary of terms In thi
Page 93 and 94: Glossary of terms • Diagnostic Pe
Page 95 and 96: Glossary of terms • Model: an abs
Page 97: Glossary of terms • System: an en
Page 100 and 101: B.2 System Data Case Study Details
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Page 104 and 105: B.5 Discretization of Implementatio
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Page 108 and 109: C.2 Model of the Power Supply Examp
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