pdf download - Software and Computer Technology - TU Delft

More documents

Recommendations

Info

5.6 MBD Implementation 3 Diagnosing the Beam Propeller Movement of the Frontal Stand Y Observations Diagnosis e pos CS P.V. POS. SP. CUR. Possible single faults Y0 0 0 0 0 0 None: entire system healthy Y1 0 0 0 0 1 !h_EXT, !h_MVR, !h_MBU Y2 0 0 0 1 0 !h_EXT, !h_MVR, !h_MBU, !h_Stand Y3 0 0 0 1 1 !h_EXT, !h_MVR, !h_MBU Y4 1 0 0 1 0 0 !h_EXT, !h_MVR, !h_MBU, !h_Stand, !h_PEU Y5 0 0 0 1 0 0 !h_EXT, !h_PEU Y6 0 0 1 0 1 !h_EXT, !h_MVR, !h_MBU Y7 0 0 1 1 0 !h_EXT, !h_MVR, !h_MBU, !h_Stand Y8 0 0 1 1 1 !h_EXT, !h_MVR, !h_MBU Y9 0 1 0 0 0 !h_PEU Y10 0 1 0 0 1 None: only multiple faults Y11 0 1 0 1 0 None: only multiple faults Y12 0 1 0 1 1 None: only multiple faults Y13 0 1 1 0 0 !h_PEU Y14 0 1 1 0 1 None: only multiple faults Y15 0 1 1 1 0 None: only multiple faults Y16 0 1 1 1 1 None: only multiple faults Y17 1 !h_EXT Table 5.7: Partial diagnosis Look-Up Table of the Beam Propeller Movement for single faults LUC_Extension has highest probability of being at false. Thus, the fact that LUC_Extension is at false is on the top of the list of both implementations. This implies that the accuracy is equal for both implementations. As a result, the diagnostic performance does not increase if MBD-2 is implemented. The benefits of the entropy calculation are clear: we humans would have chosen to implement MBD-2 instead of MBD-1. As a consequence, needless costs would be spend for the development of the tool that derives the value of ctr_speed. The entropy calculation predicts that the accuracy will not increase for future fault scenarios, and saves the higher development costs of MBD-2. 5.6 MBD Implementation 3 This section presents how to use entropy to determine which additional measurements on the system can increase the diagnostic accuracy the most. Adding measurements means increasing the observation space. The observation space can be divided in a spatial dimension and a temporal dimension [21]. The spatial dimension refers to the number of observation variables (sensors). The temporal dimension refers to the number of samples per observation variable in time. Here, the spatial dimension of the beam propeller movement system is examined. Figure 5.3 shows all components, observables and internal variables with respect to the beam propeller movement. The observation space of the model used for implementation MBD-2 is determined by all permutations of the following observables: CURRENT_ERROR, SPEED_ERROR, e_pos, POSITION_ERROR, POSVAL_ERROR and ctr_speed. It is interesting to known what variables shown in the block diagram of Figure 5.3 are best to add to this observation space. In other words, an engineer is interested what measurements of the target system improve the accuracy of the MBD outcome the most. This is interesting because there are costs for each measurement that is added to the observation space. For example, adding the variable real_speed requires that a sensor is added to the target system, the sensor signal is being logged, possibly discretized, and finally inserted 64
Diagnosing the Beam Propeller Movement of the Frontal Stand 5.6 MBD Implementation 3 into the diagnostic engine. Other variables do not require the addition of a sensor, but require the implementation of the discretizer defined in Appendix B.5 (e.g., Iset, Vact, etc.). The MBD-3 implementation is shown in Appendix C.3.6, which lists a modified version of the model in which all variables of Figure 5.3 are declared, and part of a behavioral rule. This model declares, in addition to the six variables that were already declared as observables, 8 other variables as being observable, namely I_mvr, I_to_motor, I_from_motor, real_speed, Vact, Vset, Iact and Iset. These are chosen, because it is most reasonable that these internals can be measured by means of some sensor or discretizer. The total amount of possible sensor set-ups are all permutations of theses 8 variables (e.g., [I_mvr, I_set], [real_speed], [I_mvr, I_to_motor, I_from_motor]). In order to calculate the best possible sensor set-up, given that the 6 observables of MBD-2 are already defined, the values of these 6 variables should be known. These values are different for many fault scenarios, and different values imply other values for the entropy of a particular sensor set-up. To overcome this problem, the values of these 6 observables are given the observable values defined by the fault categories. For each fault category, the set of values for the 6 observables of MBD-2 is added to the model of Appendix C.3.6 (this version of the model shows the addition of the fault scenario C1-values). Then, the entropy gain for all possible sensor set-ups is calculated for that fault category. The entropy gain of a particular sensor set-up is obtained by weighting the outcome of a fault category by the number of real-case fault scenarios that are in that particular fault category. Ideally, the calculation considers all permutations of the 8 additional observables. Because there is no tool that automates this extensive search process available, 5 sensor set-ups are arbitrarily chosen. The outcome, of the examination that is presented below, is a list of these 5 sensor set-ups that is ordered by the ability to increase diagnostic accuracy of the target system. Table 5.8 shows the entropy gain of the 5 chosen sensor set-ups in case a fault scenario in fault category C1 occurred. It shows that adding the three additional sensors, I_mvr, I_to_motor and I_from_motor, have the highest entropy gain, for this particular fault category. It is also interesting to notice that adding a sensor for the real_speed has equal entropy gain as adding the two sensors for I_from_motor and Vact. Table 5.9 shows the entropy gain of the same sensor set-ups in case a fault scenario in fault category C2 occurred. This time adding sensors for I_mvr and I_set has higher entropy gain than adding the three three sensors. Table 5.10 shows the entropy gain in case a fault scenario in fault category C3 occurred. Finally, Table 5.11 shows the entropy gain in case a fault scenario in fault category C4 occurred. Additional measurements Entropy gain I mvr, I to motor, I from motor 0.2945 I mvr, I set 0.2589 I from motor, Vact 0.2407 real speed 0.2407 I from motor 0.2407 Table 5.8: Some additional measurements for fault scenarios in fault category C1 (current error) The entropy gain of a sensor set-up is calculated by weighting the entropy gains of the Tables 5.8, 5.9, 5.10 and 5.11 by the number of times a fault scenario from that category occurred in a real-case setting. From Table B.2 the number of occurrences of each fault category (for S1-S11 and S17-S42) is calculated: • C1: 16 (CURRENT_ERROR = true) • C2: 7 (SPEED_ERROR = true) 65
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 27 and 28: 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
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 77: 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
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
Page 102 and 103: B.4 Full List of Examined Fault Sce
Page 104 and 105: B.5 Discretization of Implementatio
Page 106 and 107: C.1 The Synthetic Models that are U
Page 108 and 109: C.2 Model of the Power Supply Examp
Page 114 and 115: C.3 The Models Constructed for the
Page 128 and 129:
C.3 The Models Constructed for the
Page 131 and 132:
Appendix D State-of-the-Future Faul
show all

pdf download - Software and Computer Technology - TU Delft

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?