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3.6 Evaluation of Approaches to Fault Diagnosis Automated Fault Diagnosis this evaluation. The technique that is referred to as ’current approach’ is a combination of the approaches PMS-1, PMS-2, PMS-3 and PMS-4 that are characterized by Table 3.1. The values of all scores range from very bad (−−) to very good (++). The assessment of all scores is based on own opinion, experience and interviews with experts. The remainder of this section motivates the assessment of Table 3.4. The estimation I made is that the current approach achieves better accuracy than an approach that uses automated induction on black box information, but worse than the three other automated approaches. This is because the other these three automated approaches use information of experts, while the current approach uses information of service engineers, and ’Automated Induction (BB)’ only uses black box information. The speed of diagnosis is higher for an automated approach than for manual approaches. MBD is faster than the other automated approaches. This is in the domain of milliseconds, thus only essential when produced diagnoses are used for automated system recovery. Like accuracy, the diagnostic resolution is best when experts use their knowledge about the system. Either by applying the inference off-line (called ’Off-line inference’ in Table 3.4), or by online inference of an automated entity that uses a model made by experts (MBD). Intuitively, inference of white box information scores better on diagnostic resolution than inference of black box information. The current approach is estimated having better diagnostic resolution as inference on black box information. The context independency (denoted by ’independence’ in Table 3.4) of techniques is worse for both the current approach and the inference of black box information. As said before, the current approach depends on the skills and experience of currently employed people. ’Automated induction (BB)’ requires much data. This data must be produced by systems that have the same characteristics as systems that are diagnosed by the using the inductively constructed LUT. Furthermore, a manually inferred LUT of ’Off-line inference’ scores less on context independency than ’Automated induction (WB)’ and MBD. This is because these consistency-based models contain more knowledge. This is useful in case employees change jobs. There has been no research of the development costs of the various diagnostic techniques. For PMS, the technique ’Automated induction (BB)’ is cheapest, because the remote monitoring tools already provide most of the needed functionality. The runtime costs of the approaches has not been investigated as well. The only estimation made is that automated approaches require less runtime costs, because the labor costs are likely to be less. The approaches that use a consistency-based model score highest on adaptability. This is because changes in the design do not require time consuming and error-prone inferencing of a new LUT (’Off-line inference’ and ’Current approach’). The inductive process of ’Automated induction (BB)’ and ’Current approach’ require that systems, that implemented the new design, already produced sufficient data. The explanation facility is best for the ’Off-line inference’, ’Automated Induction (WB)’ and MBD approaches. In these approaches, the diagnoses can be explained in terms of the consistency-based model, while the current approach and ’Automated induction (BB)’ do not have such a model. MBD scores higher than ’Off-line inference’ and ’Automated induction (BB)’, because diagnoses made by ’Off-line inference’ can only be explained by the experts that implemented it, and ’Automated induction (WB)’ uses a consistency-based model that only specifies structure. As a consequence of the evaluation given by Table 3.4, MBD is chosen as a proposed approach to fault diagnosis. It is likely to improve the fault diagnosis process at PMS, because it has equal score or higher score than the current approach. Also, it scores best of all automated approaches. The following chapter introduces model-based fault diagnosis. 30
Automated Fault Diagnosis 3.6 Evaluation of Approaches to Fault Diagnosis Approach Criteria Accuracy Speed Diagnostic Resolution Independency Development Costs Runtime Costs Current approach 0 −− 0 −− − − − − Automated Induction (BB) −− + −− −− + + − −− Off-line Inference + + + 0 0 + − 0 Automated Induction (WB) + + 0 + 0 + + 0 MBD + ++ + ++ 0 + + + Adaptability Explanation Facility Table 3.4: Evaluation approaches. 31
Page 1: Automated Fault Diagnosis at Philip
Page 4 and 5: © 2006 W.M. Lindhoud. Document gen
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: Automated Fault Diagnosis 3.5 Model
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 88 and 89: BIBLIOGRAPHY [12] Johan de Kleer an
Page 91 and 92: Appendix A Glossary of terms In thi
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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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