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5.4 MBD Implementation 1 Diagnosing the Beam Propeller Movement of the Frontal Stand 58 Figure 5.3: Partial architecture of the FRUs that implement the beam propeller movement. Signals enclosed by brackets are not observable, the others are.
Diagnosing the Beam Propeller Movement of the Frontal Stand 5.4 MBD Implementation 1 5.4.2 The Modeled Structure The structure of the model is shown in Figure 5.3. In short, this block diagram describes the following. The input of the target system is the speed the user requests (V_user). The controller of the movement controls the the output of the target system: the angular speed of the frontal stand (speed). It does so, by using three control loops. Note that the fault detection mechanism is part of the architecture. It constitutes of the POSITION_ERROR, SPEED_ERROR, CURRENT_ERROR and POSVAL_ERROR signals. These signals are also chosen as being outputs of the system. During nominal behavior of the system these four error signals are false (Appendix B.3). The LYDIA model that defines the nominal behavior is listed in Appendix C.3.5. This model, that is part of the first MBD implementation, specifies the target system with a certain granularity. The chosen granularity is motivated by the following two considerations: • the choice of PMS what components can be replaced with new FRUs, and which cannot. This is the reason that the potentiometer and encoder are combined to one entity (PEU). The motor and brake are also combined (MBU), because it is impossible to replace one without the other. • the possibility to use system data to distinguish between faults. For example, a fault of the Stand could, on a lower level of abstraction, be caused by numerous anomalities: oil, a power from the environment, etc. In some cases, these ’lower’ faults can be recovered by the service engineer, but the system data cannot reveal them. Thus, these low-level faults are not represented by health variables in the model. The model listed in C.3.5 is not compositional. Thus, the structure is not explicitly modeled. This is a practical form of the model during development, but in order to allow long-term maintenance, a compositional model is much more appropriate. Appendix C.3.2 shows a compositional version of the model belonging to the MBD-1 implementation. In this model, each health variable belongs to separate component. Each component is modeled as a separate entity. The structure is defined by the top level entity of the model; FS_ Beam_ Propeller_ Movement. Each component defines part of the behavior. The behavior is discussed in the following subsection. 5.4.3 Behavior of the Model A block diagram as shown in Figure 5.3 only shows structure, and only developers of the system are able to understand how these components emit the intended behavior. The components of the compositional model LYDIA model do specify how the components implement the beam propeller movement of the frontal stand (see Appendix B.3 for a description of nominal behavior in natural language). It is a weak fault model. None is said about what happens if, for example, the MBU malfunctions. To give insight in how the models specifies the nominal behavior of the system, the writing below describes two important building blocks of the model: a control loop and an error signal. Modeling a Control Loop A very interesting issue of the beam propeller movement subsystem is that it consists of control loops. Because of their recursive nature the model should embed state and time. However, these issues are still topic of ongoing research. Therefore, the text below suggests an abstraction of a control loop that does not depend on time and state. Figure 5.4 shows successively the block diagram of one, two and three control loops. 59
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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
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 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
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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
Page 63 and 64: Chapter 5 Diagnosing the Beam Prope
Page 65 and 66: Diagnosing the Beam Propeller Movem
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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
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Page 108 and 109: 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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