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研究生:呂文斌
研究生(外文):Wen-Bin Lu
論文名稱:錯誤偵測與診斷設計於容錯電腦控制系統之應用
論文名稱(外文):Design of Error Detection and Diagnosis for Fault-Tolerant Computer-Controlled Systems
指導教授:丁鏞
指導教授(外文):Yung Ting
學位類別:博士
校院名稱:中原大學
系所名稱:機械工程研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:93
語文別:英文
論文頁數:130
中文關鍵詞:派翠西網路錯誤診斷錯誤偵測容錯模糊推理
外文關鍵詞:fuzzy reasoningError detectionerror diagnosisfault tolerancePetri nets
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本研究係以單版本軟體容錯為基礎發展一套具錯誤偵測及診斷機制(Error Detection and Diagnosis Mechanism, EDDM)之PC-Based容錯電腦控制系統。首先,利用階層式設計方式將微軟視窗作業系統及C++函式庫中所能辨識之現存各種型態的失效事件進行階層關聯分類,並藉由知識描述技術將這些已完成分類的失效事件描述成EDDM可用之知識文件,同時為了使這些失效事件的知識文件日後能容易透過網際網路進行編譯、更新及交流,本文中利用XML語法作為失效事件的知識文件內容的編譯工具。EDDM的偵測機制係使用攔截方式對被監控應用程式(Application Programming, AP)進行訊息攔截,並偵測被監控應用程式是否發生失效現象。由於攔截技術本身係屬於一種擷取模式而非中斷模式,因此訊息攔截過程中並不會直接影響到被監控應用程式或作業系統的運行。而診斷機制則是根據偵測機制所攔截到之錯誤訊息進行訊息型態與錯誤階層位置之辨識,並提供執行中的應用程式一個預測評估結果。為達成應用程式的失效事件推論決策及獲取最終的危害決策評估結果,文中是利用模糊集合定理、模糊產生式規則及派翠西網路定理(Petri net theory)的建模技術來建構應用程式失效事件的模糊診斷推理機,並簡稱此模糊診斷推理機模型為模糊推理驗證派翠西網路模型(Fuzzy Reasoning and Verification Petri Nets, FRVPNs)。根據失效事件的錯誤徵狀定義其語意變數、模糊集合及其隸屬函數,並藉由模糊產生式規則的應用來建構模糊推理過程所需的模糊規則內容,並以“IF…THEN…”的方式作為模糊規則命題的描述。再藉由派翠西網路(PNs)建模技術建構整個模糊規則決策樹(Fuzzy Rule Decision Tree, FRDT)的網格化模型,並利用FRVPNs模型來檢測模糊知識規則庫中所建立的模糊產生式規則是否存有冗餘、衝突、迴圈及死點等類型的模糊規則,並予以修正,以增進推理精確度及提高效率。
The present study attempts to develop an error detection and diagnosis mechanism (EDDM) of single version software for fault-tolerant system. The failures caused by software fault of a PC-based computer-controlled system will be focused on. Error classification is established by means of the knowledge-representation based error hierarchy design. The detection mechanism employs the hook process to capture the message in and between the various application programs and the operating system, and detects whether the monitored application program is failed. Since the hook technique uses the capture mode but not the interrupt mode. Therefore, it will not affect the executing application program or the operating system. The diagnosis mechanism can identify the failure type and location of the failure message and make predictable estimation on the executing application program. A fuzzy reasoning and verification Petri nets (FRVPNs) model is established to achieve the purpose of reasoning and decision-making the failure event of the monitored application program for the EDDM. According to the symptoms of failure event, the linguistic variables, the fuzzy sets and its membership functions, and the fuzzy reasoning rule by use of “IF…THEN…” proposition are designed for the FRVPNs model. Through the hierarchical design of the fuzzy rule tree decision (FRDT) associated with using the PN technique to transform the fuzzy reasoning rule into the PRVPNs model, the inference speed and accuracy can be improved. In addition, the inconsistent rules such as conflict, redundancy, circularity, and incompleteness etc. that likely exist in the fuzzy knowledge rule base will be verified and modified by the FRVPNs model.
CONTENTS
Abstract (In Chinese) I
Abstract (In English) III
Contents V
List of Figures VII
List of Tables X
Acronyms XI
Notation XII

Chapter 1 Preface 1
1.1 Problem Definition and Motivation 1
1.2 Related Review 2
1.3 Organization 4

Chapter 2 Introduction to Fuzzy Set and Petri Net Theories 5
2.1 Introduction to Fuzzy Set Theory 5
2.1.1 Membership Function Definition 7
2.1.2 Fuzzification Representation 11
2.1.3 Fuzzy Rules and Fuzzy Reasoning 12
2.1.4 Defuzzification Method 16
2.2 Introduction to Petri Net Theory 18
2.2.1 Formal Definition of Petri Net 19
2.2.2 Analysis of Petri Net Structure 21
2.3 Fuzzy Rule Representation by Petri Nets 24

Chapter 3 Design of the Error Detection and Diagnosis Mechanism (EDDM) Model 29
3.1 Structure of the Fault-Tolerant System and the EDDM 29
3.1.1 Configuration of the Fault-Tolerant System 29
3.1.2 Configuration of the EDDM 31
3.2 Error Classifications and Knowledge Representation 33
3.2.1 Hierarchical Classifications of Error Event 33
3.2.2 Knowledge Representation (KR) 36
3.3 Detection and Diagnosis Mechanisms 40
3.3.1 Detection Mechanism Design 40
3.3.2 Diagnosis Mechanism Design 45

Chapter 4 Design of Reasoning Process for Error Detection and Diagnosis Using Fuzzy Petri Nets 51
4.1 Fuzzy Diagnosis Reasoning Structure of EDDM 51
4.2 Representation of Fuzzy Sets and Fuzzy Rules 52
4.2.1 Definition of Fuzzy Sets and Linguistic Variables 52
4.2.2 Fuzzy Rules Representation 56
4.3 Structures and Analysis of FRDT 57
4.3.1 Structure of the FRDT 57
4.3.2 Dynamic Decision-Making Procedure of FRDT 59
4.4 FRVPNs Modeling 62
4.4.1 Definition of FRVPNs 62
4.4.2 FRVPNs Model and Dynamic Reasoning Behavior 65
4.5 FRVPNs Reasoning Strategy 68
4.5.1 FRVPNs Reasoning Algorithm for the FRDT Level_I 68
4.5.2 FRVPNs Reasoning Algorithm for the FRDT Level_II 73
4.6 Rule Verification and Modification Methodology Based on FRVPNs Model 75
4.6.1 Analysis of the FRVPNs Model 75
4.6.2 Verification and Modification Algorithm 78

Chapter 5 Simulations 93
5.1 EDDM Simulations 93
5.2 FRVPNs Simulations 98

Chapter 6 Conclusions and Future Works 103
6.1 Conclusions 103
6.2 Future Works 104

Reference 106
Curriculum Vitae 115

List of Figures
Figure 2.1 Speed MF for (a) a Crisp Set S and (b) a Fuzzy Set M p.6
Figure 2.2 Triangular MF p.8
Figure 2.3 Trapezoidal MF p.8
Figure 2.4 Core, Support, Boundary, and Crossover Point of a Fuzzy Set p.9
Figure 2.5(a) Normal Fuzzy Set and (b) Subnormal Fuzzy Set p.9
Figure 2.6(a) Convex Fuzzy Set and (b) Nonconvex Fuzzy Set p.10
Figure 2.7 The Set Intersection of Two Convex Fuzzy Sets p.10
Figure 2.8 MF Representing Imprecision in “Crisp Voltage Reading” p.11
Figure 2.9 (a) Comparison of Fuzzy Set and Crisp Reading and (b) Comparison of Fuzzy Set and Fuzzy Reading p.12
Figure 2.10 An Example of a Linguistic Variable p.13
Figure 2.11 Structure of Mamdani-Style Fuzzy Reasoning p.15
Figure 2.12 Block Diagram for a Fuzzy Reasoning System p.16
Figure 2.13 (a) First Part of Fuzzy Output (b) Second Part of Fuzzy Output (c) Union of Both Parts p.17
Figure 2.14 Dynamic Evolution of Markings of a PN p.21
Figure 2.15 Representation of the PN Structures p.23
Figure 2.16 A FPNs Structure p.25
Figure 2.17 Firing a Marked FPNs p.26
Figure 2.18 The Type-A Marked FPNs p.26
Figure 2.19 The Type-B Marked FPNs p.27
Figure 2.20 The Type-C Marked FPNs p.27
Figure 2.21 FPNs Representation of Type-D Fuzzy Rules p.28
Figure 3.1 Structure of Fault-Tolerant Computer-Controlled System p.30
Figure 3.2 Configuration of the EDDM and the correlation with MS Windows 2000 p.32
Figure 3.3 Failure Event Classifications and Causality of Relation Hierarchy of the AP p.35
Figure 3.4 Two Threads with Their Respective THREADINFO Structures p.41
Figure 3.5 Flowchart of the Detection Mechanism p.43
Figure 3.6 Time Schedule of the Detection Mechanism p.45
Figure 3.7 Flowchart of the Diagnosis Mechanism p.47
Figure 3.8 Diagram of a Prediction Example of the Reasoning Process p.50
Figure 4.1 Block Diagram of Fuzzy Diagnosis Reasoning Structure p.52
Figure 4.2 MFs for the Fuzzy Variable “Total CPU Usage” p.55
Figure 4.3 MFs for the Fuzzy Variable “Process CPU Usage” p.55
Figure 4.4 MFs for the Fuzzy Variable “Total Memory Usage” p.55
Figure 4.5 MFs for the Fuzzy Variable “Response Time” p.55
Figure 4.6 MFs for the Fuzzy Variable “Handling Time” p.55
Figure 4.7 MFs for the Fuzzy Variable “Error Hazard Level” p.55
Figure 4.8 MFs for the Fuzzy Variable “Damage Level Decision-Making” p.56
Figure 4.9 Structure of FRDT p.58
Figure 4.10 Reasoning the Sub-Object in the FRDT Level_I p.59
Figure 4.11 Reasoning the Object in the FRDT Level_II p.61
Figure 4.12 (a) Fuzzy Rules of FRDMT Level_I, II (b) FRVPNs Model (c) Marking Dynamic Reasoning Behavior p.66
Figure 4.13 The FRVPNs Model of the FRDMT Level_I p.69
Figure 4.14 The FRVPNs Model of the FRDMT Level_II p.74
Figure 4.15 Configuration of the FRDT Model p.80
Figure 4.16 Zoom of the Region A of Figure 4.15 p.82
Figure 4.17 Matrix OT of Output Transition for Figure 4.16 p.82
Figure 4.18 Zoom of the Region B of Figure 4.15 p.84
Figure 4.19 Matrix IT of Input Transition for Figure 4.18 p.84
Figure 4.20 Zoom of the Region C of Figure 4.15 p.86
Figure 4.21 Matrix IT of Input Transition for Figure 4.20 p.86
Figure 4.22 Zoom of the Region D of Figure 4.15 p.88
Figure 4.23 Matrix OT of Output Transition for Figure 4.22 p.88
Figure 4.24 Zoom of the Region E of Figure 4.15 p.90
Figure 4.25 Matrix OT of Output Transition for Figure 4.24 p.90
Figure 4.26 Dynamic Behavior of the Verification and Modification for Figure 4.15 p.91
Figure 5.1 Executing Procedures and its Information p.93
Figure 5.2 Status Diagram of the Monitored AP with a Failure Event p.94
Figure 5.3 Status Diagram for the AP Encountered Deadlock or Infinite Loops p.95
Figure 5.4 Process Name, Error Message, and KU on the Window p.96
Figure 5.5 Description of Failure Event of AP by the EDDM p.96
Figure 5.6 Error Information Record on the Window p.97
Figure 5.7 Real-Time Error Information on the Window p.97
Figure 5.8 Final Decision of the FRDT Level_I p.100
Figure 5.9 Final Decision of the FRDT Level_II p.102

List of Tables
Table 3.1 XML Frame of Object Failure KU for a Zero Divisor Case via KR p.37
Table 4.1 Linguistic Values and Ranges of Fuzzy Sets for FRVPNs p.54
Table 4.2 Parameters of Verification and Modification Algorithm p.75,76
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