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5.3 Off-line Inference by Experts Diagnosing the Beam Propeller Movement of the Frontal Stand Stand and Potmeter/Encoder and LUC _Extension (feedback). So, these components are possibly at false if POSITION_ERROR is true. It is important to notice that, these three clarifying predictions of system behavior can only be made by means of such a concise representation of the system as Figure 5.3. Without it you would have to trust the expert’s inference of system behavior. It is true that some experts know this information by head, and they use it to diagnose the fault scenarios. However, many times the information is not as comprehensive as Figure 5.3 shows. Moreover, an expert is unaware of using some information. Therefore, this knowledge is obtained by many interviews and by studying documentation [17, 16, 19]. The fault categories used in Table 5.4 can be explained by the introduced knowledge. Fault categories C1 and C2, describe resp. an unhealthy control loop and an unhealthy speed loop. The fault category C3 describes an occurrence of a POSITION_ERROR that seems to reveal an unhealthy position loop. However, the values of the Pact and Pset variables do not differ more than the threshold, and therefore the LUC_Extension falsely generates the POSITION_ERROR. It is possible that the error is caused by a malfunctioning PEU. From now on, the fact whether or not Pact and Pset differ more than the threshold is denoted by the variable e_pos. When e_pos is 1 the threshold is violated, otherwise e_pos is 0. Fault scenario C4 is an occurrence of a POSVAL_ERROR. This indicates that the PEU is broken. The symptoms described above can be described more formally as follows: CURRENT_ERROR = 1 ∨ SPEED_ERROR = 1 ∨ POSITION_ERROR = 1 ∧ e_pos = 1 ∨ POSITION_ERROR = 1 ∨ POSVAL_ERROR = 1 (5.2) The variables that are used in the specification of the symptoms are the observables on the system: CURRENT_ERROR, SPEED_ERROR, POSITION_ERROR, POSVAL_ERROR, and e_pos. The identification of the symptoms enables the construction of the table that defines the mapping of these symptoms on diagnoses. This table is presented in the next subsection. 5.3.3 Results of Off-line Inference by Experts The LUT is constructed by manually performed deductive and abductive reasoning of all possible values of the observables. Table 5.5 shows the result. For each possible observation, the table defines a mapping on a set of consistent diagnoses. For presentation purposes, the table shows only single faults. The table could be implemented by some expert system. In such an automated fault diagnosis approach, the monitored system data is searched in the table, inserted into the expert system, and the expert system produces a list of possible diagnoses. However, in the approach of this case study, the constructed LUT is just an intermediate step; Table 5.5 is used for constructing a consistency-based model. The entropy of the approach to fault diagnosis ’off-line inference’ can be calculated, by using the complete (including multiple faults) LUT. This yields the following result for the entropy (H) that experts are able to achieve: H expert = 0.9453 (5.3) The next section presents a MBD implementation, that achieves the same entropy. 56
Diagnosing the Beam Propeller Movement of the Frontal Stand 5.4 MBD Implementation 1 Y Observations Diagnosis e pos P.V. POS. SP. CUR. Possible single faults Y1 0 0 0 0 None: entire system healthy Y2 0 0 0 1 !h_EXT, !h_MVR, !h_MBU Y3 0 0 1 0 !h_Stand, !h_EXT, !h_MVR, !h_MBU Y4 0 0 1 1 !h_EXT, !h_MVR, !h_MBU Y5 1 0 1 0 0 !h_PEU, !h_Stand, !h_EXT, !h_MVR, !h_MBU Y6 0 0 1 0 0 !h_PEU, !h_EXT Y7 0 1 0 1 !h_EXT, !h_MVR, !h_MBU Y8 0 1 1 0 !h_Stand, !h_EXT, !h_MVR, !h_MBU Y9 0 1 1 1 !h_EXT, !h_MVR, !h_MBU Y10 1 0 0 0 !h_PEU Y11 1 0 0 1 None: only multiple faults Y12 1 0 1 0 None: only multiple faults Y13 1 0 1 1 None: only multiple faults Y14 1 1 0 0 !h_PEU Y15 1 1 0 1 None: only multiple faults Y16 1 1 1 0 None: only multiple faults Y17 1 1 1 1 None: only multiple faults Table 5.5: Partial diagnosis look-up table of the Beam Propeller Movement for single faults 5.4 MBD Implementation 1 This section presents the third step of the case study approach. In this step, the first MBD implementation (MBD-1), that reproduces the results and entropy that experts are able to achieve, is presented. The first part of this section discusses the model. Then, the diagnosis and entropy are discussed. 5.4.1 The Modeled Information In this subsection, the model of the first MBD implementation is constructed. As explained before, the LUT that experts derived is used as valuable information for constructing a consistency-based model. Other sources of information are tools and documentation that are developed and written at PMS. In summary, the following sources of information have been used for the construction of the model that is presented in this section: • The result of abductive and deductive reasoning by experts, as presented by the LUT shown in Table 5.5. • Documentation about the meaning of the system data (see Table B.1). • Technical drawings. • Fault Isolation Procedures. • Some design documents, specific to the beam propeller movement [17]. • Interviews with experts on the system. 57
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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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Page 58 and 59: 4.5 Entropy Gain Model-Based Fault
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Page 88 and 89: BIBLIOGRAPHY [12] Johan de Kleer an
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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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