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空間故障樹理論與系統可靠性分析(英文版) 版權信息
- ISBN：9787030660343
- 條形碼：9787030660343 ; 978-7-03-066034-3
- 裝幀：一般膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
空間故障樹理論與系統可靠性分析(英文版) 內容簡介
本書總結了這六年來空間故障樹理論發展的核心成果。將這些內容形成體系，并搶先發售以英文專著形式出版。空間故障樹理論是一套完整的，獨立的系統可靠性分析框架；具有良好的適應性、擴展性和通用性，是自成體系的方法論。本書以理論建立和推導為主，并輔以算例分析。按照理論發展過程編排結構。給出了空間故障樹的基本概念、思想、方法、結論等，并有機地貫串在一起，形成層次合理的理論體系，既內容豐富，又顯得條理清楚。為體現方法本身特點和分析過程，使用了較為簡單的例子。以使讀者了解思想，不糾結于復雜的過程，進而突出方法本身。同時本書面向安全領域和系統可靠性研究人員，因此我們更注重于可靠性研究的思想傳遞，以便開闊研究思路。
空間故障樹理論與系統可靠性分析(英文版) 目錄
Contents
Content Summary
Introduction
Chapter 1 Introduction 1
1.1 Purpose and Significance 1
1.2 Summary of Research and Problems 2
1.2.1 Fault Tree Research 2
1.2.2 Multi-Factor Influence and Fault Big Data 3
1.2.3 System Function Structure Analysis and Factor Space
1.2.4 System Reliability and Influencing Factors
1.2.5 loud Model and Similarity 8
1.2.6 Object Calassification and Similarity 11
1.3 Deficiency of System Reliability 12
1.4 Main Research Content 14
References 16
Chapter 2 Continuous Space Fault Tree 23
2.1 Concepts of CSFT 24
2.2 Fault Probability Distribution 26
2.2.1 Component Fault Probability Distribution 26
2.2.2 System Fault Probability Distribution 28
2.3 Importance Distributions 35
2.3.1 Probability Importance Distribution 35
2.3.2 Criticality Importance Distribution 37
2.4 System Fault Probability Distribution Trend 50
2.5 Calculation of MTLα 50
2.6 Conclusions 53
References 59
Chapter 3 Discrete Space Fault Tree 61
3.1 Discrete Space Fault Tree 61
3.2 Significance of DSFT Modifed Using Fuzzy Structured Element 65
3.3 Factor Projection Fitting Metho 66
3.4 Constructions and Applications of EDSFT 69
3.4.1 E-Characteristic Function 69
3.4.2 E-Component Fault Probability Distribution 73
3.4.3 E-System Fault Probability Distribution 74
3.4.4 E-Probability Importance Distribution 74
3.4.5 E-Criticality Importance Distribution 75
3.4.6 E-System Fault Probability Distribution Trend 75
3.4.7 E-Component Domain Importance 76
3.4.8 E-Factor Importance Distribution 78
3.4.9 EFactor Joint Importance Distribution 79
3.5 Conclusions 79
References 80
Chapter 4 Inward Analysis of System Factor Structure 83
4.1 Inward Analysis of System Factor Structure 84
4.2 Human-Machine Cognition 86
4.3 Table Method 87
4.4 Classification Reasoning Method 88
4.5 Mathematical Description of Cassification Reasoning Method 93
4.6 Item-By-Item Analyses 94
4.7 Mathematical Description of Item- By-Item Analyses 95
4.8 Conclusions 98
References 99
Chapter 5 Function Structure Analysis and Factor Space 101
5.1 Factor Analysis Method of Function Structue 103
5.1.1 Factors and Dimension Variabitity 103
5.1.2 Function Structure Analysis Space 104
5.2 Factor Logic Description of Function Structure 105
5.2.1 Axiom System of Function Structure Analyis 105
5.2.2 Minimization Method of System Function Structure 109
5.3 Analysis of System Function Structure 110
5.3.1 Analysis with Incomplete Information 111
5.3.2 Analysis with Complete Information 113
Reference 117
Chapter 6 System Reliability with Influencing Factors 119
6.1 Methodology of Concepts and Definition 121
6.2 Analysis of Relationship between Reliability and Infuencing Factors 124
6.2.1 Random Variable Decomposition Fomula 124
6.2.2 Causal Relationship Reasoning 126
6.2.3 Causal Concept Extraction 127
6.2.4 Background Relationship Analysis 127
6.2.5 Factor Dimension Reduction 128
6.2.6 Compression of Fault Probability Distribution 130
6.3 Algorithm Applation 132
6.3.1 Random Variable Decomposition Formula 132
6.3.2 Causal Relationship Reasoning 136
6.3.3 Causal Concept Extraction 144
6.3.4 Background Relationship Analysi 147
6.3.5 Factor Dimension Reduction 150
6.3.6 Cormpression of Fault Probability Distribution 153
6.4 Conclusions 156
References 158
Chapter 7 Cloudiation Space Fault Tee 161
7.1 Definitions of SFT 163
7.2 Construction of Cloudization Space Fault Tree 163
72.1 Basis of CLSFT 163
7.2.2 Cloudization Fauit Pobability Distribution 164
7.2.3 Cloudization Fauit Pobability Distribution Trend 165
7.2.4 Cloudization Importance Distribution Probability and Criticality 165
7.2.5 Cloudization Factor Importance and Joint Importance Distribution 166
7.2.6 Cloudization Component Domain Importance 167
7.2.7 Cloudization Path Set Domain and Cut Set Domain 168
7.2.8 Uncertainty Analysis of Rlibility Data 168
7.3 Example Analysis 169
7.3.1 Cloudization Fault Probability Distribution 170
7.3.2 Cloudization Fault Probability Distribution Trend 172
7.3.3 Cloudization Importance Distribution Probability and Criticality 178
7.3.4 Cloudization Importance Distribution of Factor and Factor Joint 181
7.3.5 Cloudization Component Domain Importance 185
7.3.6 Cloudization Path Set Domain and Cut Set Domain 187
73.7 Uncertainty Analysis of Reliability Data 187
7.4 Conclusions 196
References 196
Chapter 8 Cloud Similarity 199
8.1 Similarity Algorithms of Cloud Model 199
8.2 Cloud Similarity Computation Based on Envelope 201
8.3 Algorithm Application 203
8.4 Analyses of Algorithm Advantage 205
8.5 Conclusions 206
References 206
Chapter 9 Clustering Analysis and Similarity 207
9.1 Preliminary K nowledge 207
9.2 Concepts and Properties of Attribute Circle
Content Summary
Introduction
Chapter 1 Introduction 1
1.1 Purpose and Significance 1
1.2 Summary of Research and Problems 2
1.2.1 Fault Tree Research 2
1.2.2 Multi-Factor Influence and Fault Big Data 3
1.2.3 System Function Structure Analysis and Factor Space
1.2.4 System Reliability and Influencing Factors
1.2.5 loud Model and Similarity 8
1.2.6 Object Calassification and Similarity 11
1.3 Deficiency of System Reliability 12
1.4 Main Research Content 14
References 16
Chapter 2 Continuous Space Fault Tree 23
2.1 Concepts of CSFT 24
2.2 Fault Probability Distribution 26
2.2.1 Component Fault Probability Distribution 26
2.2.2 System Fault Probability Distribution 28
2.3 Importance Distributions 35
2.3.1 Probability Importance Distribution 35
2.3.2 Criticality Importance Distribution 37
2.4 System Fault Probability Distribution Trend 50
2.5 Calculation of MTLα 50
2.6 Conclusions 53
References 59
Chapter 3 Discrete Space Fault Tree 61
3.1 Discrete Space Fault Tree 61
3.2 Significance of DSFT Modifed Using Fuzzy Structured Element 65
3.3 Factor Projection Fitting Metho 66
3.4 Constructions and Applications of EDSFT 69
3.4.1 E-Characteristic Function 69
3.4.2 E-Component Fault Probability Distribution 73
3.4.3 E-System Fault Probability Distribution 74
3.4.4 E-Probability Importance Distribution 74
3.4.5 E-Criticality Importance Distribution 75
3.4.6 E-System Fault Probability Distribution Trend 75
3.4.7 E-Component Domain Importance 76
3.4.8 E-Factor Importance Distribution 78
3.4.9 EFactor Joint Importance Distribution 79
3.5 Conclusions 79
References 80
Chapter 4 Inward Analysis of System Factor Structure 83
4.1 Inward Analysis of System Factor Structure 84
4.2 Human-Machine Cognition 86
4.3 Table Method 87
4.4 Classification Reasoning Method 88
4.5 Mathematical Description of Cassification Reasoning Method 93
4.6 Item-By-Item Analyses 94
4.7 Mathematical Description of Item- By-Item Analyses 95
4.8 Conclusions 98
References 99
Chapter 5 Function Structure Analysis and Factor Space 101
5.1 Factor Analysis Method of Function Structue 103
5.1.1 Factors and Dimension Variabitity 103
5.1.2 Function Structure Analysis Space 104
5.2 Factor Logic Description of Function Structure 105
5.2.1 Axiom System of Function Structure Analyis 105
5.2.2 Minimization Method of System Function Structure 109
5.3 Analysis of System Function Structure 110
5.3.1 Analysis with Incomplete Information 111
5.3.2 Analysis with Complete Information 113
Reference 117
Chapter 6 System Reliability with Influencing Factors 119
6.1 Methodology of Concepts and Definition 121
6.2 Analysis of Relationship between Reliability and Infuencing Factors 124
6.2.1 Random Variable Decomposition Fomula 124
6.2.2 Causal Relationship Reasoning 126
6.2.3 Causal Concept Extraction 127
6.2.4 Background Relationship Analysis 127
6.2.5 Factor Dimension Reduction 128
6.2.6 Compression of Fault Probability Distribution 130
6.3 Algorithm Applation 132
6.3.1 Random Variable Decomposition Formula 132
6.3.2 Causal Relationship Reasoning 136
6.3.3 Causal Concept Extraction 144
6.3.4 Background Relationship Analysi 147
6.3.5 Factor Dimension Reduction 150
6.3.6 Cormpression of Fault Probability Distribution 153
6.4 Conclusions 156
References 158
Chapter 7 Cloudiation Space Fault Tee 161
7.1 Definitions of SFT 163
7.2 Construction of Cloudization Space Fault Tree 163
72.1 Basis of CLSFT 163
7.2.2 Cloudization Fauit Pobability Distribution 164
7.2.3 Cloudization Fauit Pobability Distribution Trend 165
7.2.4 Cloudization Importance Distribution Probability and Criticality 165
7.2.5 Cloudization Factor Importance and Joint Importance Distribution 166
7.2.6 Cloudization Component Domain Importance 167
7.2.7 Cloudization Path Set Domain and Cut Set Domain 168
7.2.8 Uncertainty Analysis of Rlibility Data 168
7.3 Example Analysis 169
7.3.1 Cloudization Fault Probability Distribution 170
7.3.2 Cloudization Fault Probability Distribution Trend 172
7.3.3 Cloudization Importance Distribution Probability and Criticality 178
7.3.4 Cloudization Importance Distribution of Factor and Factor Joint 181
7.3.5 Cloudization Component Domain Importance 185
7.3.6 Cloudization Path Set Domain and Cut Set Domain 187
73.7 Uncertainty Analysis of Reliability Data 187
7.4 Conclusions 196
References 196
Chapter 8 Cloud Similarity 199
8.1 Similarity Algorithms of Cloud Model 199
8.2 Cloud Similarity Computation Based on Envelope 201
8.3 Algorithm Application 203
8.4 Analyses of Algorithm Advantage 205
8.5 Conclusions 206
References 206
Chapter 9 Clustering Analysis and Similarity 207
9.1 Preliminary K nowledge 207
9.2 Concepts and Properties of Attribute Circle
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空間故障樹理論與系統可靠性分析(英文版) 節選
Chapter 1 Introduction 1.1 Purpose and Significance System reliability theory is one of the basic theories of safety science. Derived from system engineering， system reliability is mainly concerned with the possibility of system faults and accidents. Owing to improvements in modern science and indus-trialisation， to pursue greater economic and strategic goals， some countries have intensified their research and established large or super-large systems to meet their requirements. However， it was found that a decline in reliability occurs during the operation of a system with an increase in system complexity. In this case， the original problem is that the lessons learned after an accident cannot meet the requirements of the present system safety. Because the research method of the problem is primarily applicable to a low value of system， with low system reliability and no critical con-sequences of a fault， such a research approach is not significant for today's large-scale and extremely complex systems. Therefore， during the 1950s， Britain and the US first proposed the concept of safety system engineering. At this time， some concepts of system engineering were introduced into the field of safety， particularly a reliability analysis method， and applied in the miltary and aerospace fields. Safety system engineering is， therefore， one of the basic aspects of safety science. Safety system engineering and system reliability analyses have since been developed under the conditions of relatively simple and low complexity systems and limited data scales. However， with the development of big data technology， in telli-gent science， system science， and related mathematical theories， existing system reliability analysis methods have also exposed certain problems， such as big data processing; reliability causation， stability， and reverse engineering; and the description of reliability changes. At the same time， existing system reliability analysis methods are mostly targeted at specific systems. Although such an analysis is effective， there has been a lack of abstraction at the system level， making it difficult to meet the required universality， scalability and adaptability. Therefore， the system reliability analysis method that achieves the above capabilities and meets future technological requirements is needed. It is， therefore， necessary to combine the system reliability analysis with intelligent science and big data technology. Space fault tree theory (SFT) [1] is a systerm reliability analysis method proposed by the authors in 2012. After some years of development， the preliminarily foun-dation of the SFT theory framework has been completed， which can satisfy the reliability analysis of a simple system， including big data processing， reliability causality， reliability stability， reliability reverse engineering， and a reliability change description，and achieves high universality， extensibility， and adaptability. Its development process integrates intelligent science and big data processing technology， including factor space theory [2]， fuzzy structured element theory [3]， and cloud model theory [4]， among others. Although some problems remain， the SFT still has adequate room to solve these issues through further development. We hope this book will broaden the basic research field of safety science， enabling readers to better understand SFT theory， factor space theory， and their role in the system reliability analysis， soeking a thoretical development of reliability adapted to intelligent science and big data technology. 1.2 Summary of Research and Problems 1.2.1 Fault Tree Research The fault tree is an important aspect of safety system engineering and plays a crucial role in the analysis of system safety and reliability in several industries. It has been widely used and studied around the world as a systermatic scientific method. The applications of the fault tree and studies conducted in different fields are reviewed in the following chapter. In medical research， the fault tree has been applied to the control of hand， foot， and mouth diseases [5]. For the safety of a laboratory-scale bioreactor， the fault tree analysis method was used to deal with hydrogen sulphide biotreatment [6]. In research into uncertainty， the problem was studied using fault tree [7. In addition， the decision tree method has been used to represent uncertainty based on proba-bility [8]. An uncertainty analysis of fault tree model based on basic events was also developed [9]. In addition， uncertainty in fault tree analysis was processed using a hybrid probabilistic-possibilistic framework [10]. In a study on system reliability analysis， a real-time systern analysis method based on fault tree was proposed [11]. A study on a non-repairable system was also conducted using dynamic fault tree and priority AND gates [12]. In an analysis of system safety， an extended fault tree was used to analyse the
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