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3.2 Automated Induction using Black Box Information Automated Fault Diagnosis Figure 3.2: Power Supply - Black Box view. 3.2 Automated Induction using Black Box Information Automated induction using black box information can be automated by a data mining technique. This approach requires three things. Firstly, a technique for observing the system 3 . Secondly, a way to determine the values of observables at the moment a failure occurred 4 . Thirdly, determination of what component was actually broken for a particular failure 5 . If these three pieces of information are available it is possible to search correlations between values of observables and adjudged broken components. Figure 3.2 shows a black box view on the power supply example system. The only information that is known are the input and output observables. Recall the example fault scenario in which we considered the broken CableB. Suppose that historical data extracted from the job sheets shows that tens of times CableB has been replaced. This is a realistic scenario considering Philips Medical Systems has hundreds of Cardio-Vascular systems all over the world. Suppose all repair actions of the service engineers actually recovered these failures of the power supply. Then, the log of each system that encountered such a failure, contains the following observation: status_Chiller = true status_FD = true status_TBCB = false status_CRCB = false status_Collimator = false A well written data mining program would easily find this (linear) correlation between this observation and a replacement of CableB. Then, a supporting diagnostic engine could use found correlations to build a LUT (analogous to Table 3.2) and use it in future cases to produce diagnoses. Another occurrence of the listed observation could be looked up in the table when the historical data is sufficient. This way, the work of the service engineer is reused. 3.3 Off-line Inference This section presents the approach to fault diagnosis in which experts apply the inference of the LUT beforehand. Again, the example of the power supply is used to improve concrete understanding of this technique. The idea is to take advantage of experts their knowledge of the system. Given 3 PMS has such a technique, namely ServiceWAX [2]. 4 At PMS, the Philips Cardio-Vascular System often logs errors that can be used to identify the time of a failure. 5 Not possible at PMS, although a service engineer documents his/her actions in a job sheet (see Chapter 6). 26
Automated Fault Diagnosis 3.3 Off-line Inference Figure 3.3: Partial architecture of the power supply (repetition of Figure 2.2). observations on the system, most practically embedded in the log, an expert is able to draw more conclusions about possible malfunctioning components than the average service engineer. Let us now consider the power supply, as depicted in Figure 3.3. Suppose the on/off -stati of the five components on the right of the figure could be known by examining the log 6 . Also, the on/off - status of the entire system is known by means of the log. Together, this input and the five outputs constitute the observations upon the system. These are all in the boolean domain, where true denotes that a component is on and false denotes that a component is off. Once the observables are known, the next step is to ’look for the symptoms in the observables’. For example, even a nonexpert understands that if the operator switches on the system, all components should eventually be on too. So, every valuation of the boolean observables, that is does not meet this expectation, is a symptom of misbehavior. Let V be the set of all valuations, a symptom s is then: ∀ s ∈ V | not ( status FD = status T BCB = status CRCB = status Collimator = status Chiller = StartU p ) (3.3) However, the only conclusion that can be drawn from this symptom is that ’something’ is wrong with the power supply. It is not possible to pinpoint to a subset of the system’s components. On the 6 Currently, the stati of the Flat Detector, Collimator and Chiller are logged. Enabling the logging of the other two, the TBCB and CRCB, would require minor adjustments of the system. 27
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
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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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Page 58 and 59: 4.5 Entropy Gain Model-Based Fault
Page 60 and 61: 4.6 MBD on the Power Supply Model-B
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Page 65 and 66: Diagnosing the Beam Propeller Movem
Page 83 and 84: Chapter 6 Conclusions and Recommend
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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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