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Risk-Based Engineering: An Integrated Approach to Complex SystemsSpecial Reference to Nuclear Plants 2018 ed. [Kõva köide]

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  • Formaat: Hardback, 568 pages, kõrgus x laius: 235x155 mm, kaal: 1045 g, 59 Illustrations, color; 82 Illustrations, black and white; XXII, 568 p. 141 illus., 59 illus. in color., 1 Hardback
  • Sari: Springer Series in Reliability Engineering
  • Ilmumisaeg: 01-May-2018
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811300887
  • ISBN-13: 9789811300882
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Risk-Based Engineering: An Integrated Approach to Complex SystemsSpecial Reference to Nuclear Plants 2018 ed.Suurem pilt
  • Formaat: Hardback, 568 pages, kõrgus x laius: 235x155 mm, kaal: 1045 g, 59 Illustrations, color; 82 Illustrations, black and white; XXII, 568 p. 141 illus., 59 illus. in color., 1 Hardback
  • Sari: Springer Series in Reliability Engineering
  • Ilmumisaeg: 01-May-2018
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811300887
  • ISBN-13: 9789811300882
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811300882.html
Märksõnad:
The book comprehensively covers the various aspects of risk modeling and analysis in technological contexts. It pursues a systems approach to modeling risk and reliability concerns in engineering, and covers the key concepts of risk analysis and mathematical tools used to assess and account for risk in engineering problems. The relevance of incorporating risk-based structures in design and operations is also stressed, with special emphasis on the human factor and behavioral risks.





The book uses the nuclear plant, an extremely complex and high-precision engineering environment, as an example to develop the concepts discussed. The core mechanical, electronic and physical aspects of such a complex system offer an excellent platform for analyzing and creating risk-based models. The book also provides real-time case studies in a separate section to demonstrate the use of this approach. There are many limitations when it comes to applications of risk-based approaches to engineeringproblems. The book is structured and written in a way that addresses these key gap areas to help optimize the overall methodology.





This book serves as a textbook for graduate and advanced undergraduate courses on risk and reliability in engineering. It can also be used outside the classroom for professional development courses aimed at practicing engineers or as an introduction to risk-based engineering for professionals, researchers, and students interested in the field.
1 Introduction
1(14)
1.1 Introduction
1(1)
1.2 Historical Perspective on Probabilistic Risk Assessment and Risk-Based Applications
2(1)
1.3 Integrated Risk-Based Engineering Approach
3(2)
1.4 Factor of Safety and Uncertainty
5(2)
1.5 Basic Framework for Integrated Risk-Based Engineering
7(1)
1.6 Major Elements of Integrated Risk-Based Engineering
8(4)
References
12(3)
2 Risk Characterization
15(16)
2.1 Background
15(1)
2.2 Definition of "Risk"
16(2)
2.3 Risk Characterization
18(4)
2.3.1 Risk Characterization Policy and Principles
20(1)
2.3.2 Major Elements of Risk Characterization
20(1)
2.3.3 Roles of People and Organizations
21(1)
2.4 Risk Assessment Techniques
22(3)
2.4.1 Failure Mode Effect Analysis (FMEA)
22(1)
2.4.2 Hazard and Operability (HAZOP) Analysis
23(1)
2.4.3 Probabilistic Risk Assessment (PRA)
23(1)
2.4.4 Quantitative Risk Assessment
24(1)
2.4.5 Other Risk Assessment Approaches
24(1)
2.5 Risk Metrics
25(4)
References
29(2)
3 Probabilistic Approach to Reliability Engineering
31(40)
3.1 Introduction
31(1)
3.2 Life Characteristics: The Bathtub Curve
32(2)
3.3 Probability Theory: Main Concepts
34(6)
3.3.1 Reliability
34(1)
3.3.2 Derivation of Reliability Function from the First Principle
35(3)
3.3.3 Reliability Characteristics
38(2)
3.4 Probability Distribution Functions
40(16)
3.4.1 Continuous Distribution Function
41(9)
3.4.2 Discrete Distributions
50(3)
3.4.3 Joint Probability and Marginal Distribution
53(1)
3.4.4 Determining Applicable Distribution
54(2)
3.5 Statistical Estimation of Failure Rate
56(6)
3.5.1 Point Estimate
56(5)
3.5.2 Confidence Interval Estimation
61(1)
3.6 Goodness-of-Fit Test
62(4)
3.6.1 Chi-Square Test
63(2)
3.6.2 Kolmogorov--Smirnov Test
65(1)
3.7 Regression Analysis
66(3)
References
69(2)
4 System Reliability Modeling
71(44)
4.1 Background and Overview
71(1)
4.2 Reliability Block Diagram
72(4)
4.2.1 Series System
72(2)
4.2.2 Parallel Configurations
74(1)
4.2.3 Complex Configurations
75(1)
4.3 Failure Mode and Effects Analysis
76(5)
4.4 Fault Tree Analysis
81(9)
4.4.1 Basic Entities in a Fault Tree
82(3)
4.4.2 Fault Tree Analysis: General Considerations
85(2)
4.4.3 Quantitative Analysis
87(3)
4.5 Event Tree Analysis
90(3)
4.6 Markov Model
93(8)
4.6.1 Markov Model for a Single-Component Non-repairable System
94(1)
4.6.2 Markov Model for a Repairable System
95(6)
4.7 Advanced Approaches in System Analysis: An Overview
101(11)
4.7.1 Dynamic Fault Tree
101(4)
4.7.2 Dynamic Event Tree Analysis
105(3)
4.7.3 Binary Decision Diagram
108(4)
References
112(3)
5 Life Prediction
115(26)
5.1 Introduction
115(1)
5.2 Literature Review
116(3)
5.3 Major Steps in Life Prediction
119(1)
5.4 Material Properties and Component Characterization for Life Testing
120(2)
5.4.1 Core SSCs
121(1)
5.4.2 Semi-Integral SSCs
121(1)
5.4.3 Repairable and Replaceable Systems
121(1)
5.4.4 Electrical Systems
122(1)
5.5 Definition of Failure
122(1)
5.6 Material Degradation and Its Characterization
123(3)
5.6.1 Microstructure
125(1)
5.6.2 Chemical Composition
125(1)
5.6.3 Role of Crystal Structure in Material Failure
125(1)
5.7 Life Prediction/Assessment Approaches
126(12)
5.7.1 Nondestructive Testing (NDT)
127(2)
5.7.2 Life Testing
129(1)
5.7.3 Highly Accelerated Stress Screening
130(6)
5.7.4 Simulation-Based Approaches
136(1)
5.7.5 Prognostics and Health Management
137(1)
5.8 Conclusions
138(1)
References
138(3)
6 Probabilistic Risk Assessment
141(96)
6.1 Introduction
141(1)
6.2 Basic Elements of Risk
142(1)
6.3 Three Levels of PRA
143(2)
6.4 Role of PRA for Risk-Based Applications
145(3)
6.5 Quality in PRA and Its Applications
148(4)
6.6 General Elements of PRA
152(6)
6.6.1 Objectives of PRA
152(2)
6.6.2 Scope of PRA
154(1)
6.6.3 Limited- and Full-Scope Level 1 PRA
155(3)
6.7 Methodology for Limited-Scope Level 1 PRA
158(28)
6.7.1 Organizational and Management
158(2)
6.7.2 Plant Familiarization
160(1)
6.7.3 Identification of Plant Hazards and Formulation of a List of Applicable Initiating Events
161(1)
6.7.4 Initiating Event Analysis
162(1)
6.7.5 Accident Sequence Analysis
163(1)
6.7.6 System Modeling
164(5)
6.7.7 Failure Criteria Evaluation
169(2)
6.7.8 Data Collection and Analysis
171(3)
6.7.9 Initiating Event Frequency Quantification
174(1)
6.7.10 Major Component Categories and Model for Estimating Unavailability
174(1)
6.7.11 Dependent Failure
175(6)
6.7.12 Human Reliability Analysis
181(1)
6.7.13 Accident Sequence Quantification
181(2)
6.7.14 Uncertainty Analysis
183(1)
6.7.15 Sensitivity Analysis
183(1)
6.7.16 Importance Analysis
184(1)
6.7.17 Core Damage Frequency-Related Aspects and Formulation of Results and Their Interpretation
184(2)
6.7.18 Documentation
186(1)
6.8 Beyond Limited-Scope PRA---Other Major Modules for Full-Scope Level 1 PRA
186(32)
6.8.1 Low-Power and Shutdown PRA
187(10)
6.8.2 Fuel Storage Pool PRA
197(2)
6.8.3 Internal Hazards
199(10)
6.8.4 External Hazards
209(9)
6.9 Level 2 PRA
218(5)
6.9.1 Background
218(1)
6.9.2 Level 2 PRA Methodology
219(4)
6.10 Level 3 PRA
223(8)
6.10.1 Background
223(1)
6.10.2 Overview of Methodology
224(1)
6.10.3 Source Term
225(1)
6.10.4 Meteorological Data and Sampling
225(1)
6.10.5 Agricultural and Population Data
225(1)
6.10.6 Atmospheric Dispersion and Propagation
226(1)
6.10.7 Exposure Pathways
227(1)
6.10.8 Health Effects
228(1)
6.10.9 Counter Measures
228(1)
6.10.10 Economic Losses and Public Conscience
229(1)
6.10.11 Results and Applications
229(2)
6.11 Conclusions and Final Remark
231(1)
References
231(6)
7 Risk-Based Design
237(34)
7.1 Introduction
237(1)
7.2 Evolution of Risk-Based Design Approach---A Review
238(2)
7.3 The Approach
240(1)
7.4 Salient Features of Risk-Based Design
241(1)
7.5 Major Elements of Risk-Based Design
242(10)
7.5.1 Identification of Safety and Functional Objectives
243(1)
7.5.2 Quality Assurance Program
244(1)
7.5.3 Postulation of Events and Hazards
244(1)
7.5.4 Formulation of an Integrated Design Framework
245(1)
7.5.5 Evaluation of SSCs Failure Criteria
246(1)
7.5.6 Characterization of Risk and Uncertainty Goals/Targets
247(1)
7.5.7 Structural Safety Margin Assessment
247(1)
7.5.8 Evaluation of Defense in Depth
248(1)
7.5.9 Surveillance/Prognostics and Health Management Program
248(1)
7.5.10 FMEA and Root Cause Analysis
249(1)
7.5.11 Human Factor Considerations
249(1)
7.5.12 Identification and Prioritization of Design Issues
250(1)
7.5.13 Evaluation of Plant/System Configuration
251(1)
7.5.14 Documentation: Safety Reports and Formulation of Technical Specifications
251(1)
7.6 Higher-Level Modeling: Probabilistic Risk Assessment
252(2)
7.7 Lower-Level Modeling: Structural Probabilistic Methods
254(12)
7.7.1 Structural Reliability: Stress/Strength Concept
254(3)
7.7.2 Derivation of Reliability Expression for Stress/Strength Interference
257(2)
7.7.3 Selected Structural-Based Methods
259(7)
7.8 Major Supporting Tools
266(1)
7.9 Codes and Standards
266(1)
7.10 Case Study: Use of Available Safety Margins to Demonstrate Reactor Safety
267(1)
7.11 Summary
267(1)
References
268(3)
8 Fatigue and Fracture Risk Assessment: A Probabilistic Framework
271(20)
8.1 Introduction
271(2)
8.2 Fatigue and Fracture: Background
273(1)
8.3 Deterministic Approach
274(4)
8.3.1 S-N Approach
275(1)
8.3.2 Fracture Mechanics Approaches
276(2)
8.4 Probabilistic Approaches
278(11)
8.4.1 Probabilistic Tools and Methods
279(1)
8.4.2 An Overview of Probabilistic Fatigue Reliability Models
279(1)
8.4.3 P-S-N Approach
280(1)
8.4.4 Probabilistic Fracture Mechanics Approach
281(6)
8.4.5 Risk Assessment and Impact Analysis
287(2)
8.5 Conclusion and Remarks
289(2)
References
9 Uncertainty Modeling
291(22)
9.1 Introduction
291(1)
9.2 Treatment of Uncertainty: A Historical Perspective
292(2)
9.3 Risk-Based Methods and Uncertainty Characterization
294(2)
9.3.1 Major Features of Risk-Based Approach Relevant to Uncertainty Characterization
294(1)
9.3.2 Major Issues for Implementation of Integrated Risk-Based Engineering Approach
295(1)
9.4 Uncertainty Analysis in Support of IRBE: A Brief Overview
296(6)
9.4.1 Risk and Uncertainty
296(1)
9.4.2 Taxonomy of Uncertainty
296(3)
9.4.3 Overview of Approaches for Uncertainty Modeling
299(2)
9.4.4 Uncertainty Propagation
301(1)
9.5 Decisions Under Uncertainty
302(5)
9.5.1 Engineering Design and Analysis
302(1)
9.5.2 Management of Operational Emergencies
303(3)
9.5.3 Regulatory Reviews
306(1)
9.6 Codes, Guides, and References on Uncertainty Characterization
307(1)
9.7 Summary and Conclusions
308(1)
References
309(4)
10 Human Reliability
313(62)
10.1 Introduction
313(2)
10.2 A Brief Overview of Human Reliability Techniques
315(5)
10.3 Motivation for the CQB-Based Human Reliability Approach
320(4)
10.3.1 Why We Need a New Approach
320(1)
10.3.2 What Is New in the CQB Approach
321(1)
10.3.3 Proposed Human Model for CQB
322(1)
10.3.4 How the CQB-Based Human Reliability Approach Works
323(1)
10.4 Basic Philosophy and Supporting Input for the CQB Model
324(8)
10.4.1 Consciousness
324(2)
10.4.2 Cognition
326(1)
10.4.3 Conscience
327(1)
10.4.4 Brain and Brain Waves
327(4)
10.4.5 Inter-relationship of Basic CQB Elements
331(1)
10.5 CQB-Based Framework
332(22)
10.5.1 Fundamental Tenets of CQB Approach
332(4)
10.5.2 The Integrated Human Model in CQB
336(2)
10.5.3 Stress-Strength Model
338(2)
10.5.4 Human Performance Influencing Factors (HPIS) in CQB
340(10)
10.5.5 CQB Mathematical Model
350(4)
10.6 The CQB Methodology
354(16)
10.6.1 Plant/Facility Familiarization
355(1)
10.6.2 Identification of Human Error Events
356(2)
10.6.3 Screening
358(1)
10.6.4 Definition of the Event
359(1)
10.6.5 Event Characterization
360(1)
10.6.6 Qualitative Assessment
361(2)
10.6.7 Data Collection and Analysis
363(3)
10.6.8 Quantitative Analysis
366(1)
10.6.9 Uncertainty Analysis
367(2)
10.6.10 Identification of Major Human Risk Contributors
369(1)
10.6.11 Assessment of Impact of Human Error in PRA
370(1)
10.7 Tools and Approaches to Reduce Human Error Probability
370(2)
10.7.1 Deployment of Operator Support Systems
370(1)
10.7.2 Reduction in Human Action by Automation
371(1)
10.7.3 Modification to Secondary Factors
371(1)
10.7.4 Simulator-Based Training
371(1)
10.8 Conclusions and Remarks
372(1)
References
372(3)
11 Digital System Reliability
375(42)
11.1 Introduction
375(1)
11.2 A Brief Overview
376(2)
11.3 Design for Reliability
378(11)
11.3.1 Governing Design Considerations
378(6)
11.3.2 Formulation of System Requirements and Constraints
384(1)
11.3.3 Postulating the Life Cycle Environment
384(1)
11.3.4 Supply Chain Management and Quality Assurance
385(1)
11.3.5 Failure Mode, Mechanism, and Effect Analysis (FMMEA)
386(1)
11.3.6 Manufacturing Issues
386(1)
11.3.7 Special Safety Issues
387(2)
11.4 Risk-Based Modeling for Design Evaluation: A Probabilistic Approach
389(22)
11.4.1 Background
390(1)
11.4.2 Limitations of the Traditional Approach
390(1)
11.4.3 Failure Mode Taxonomy for Digital Systems
391(2)
11.4.4 Reference System Description
393(2)
11.4.5 Technical Requirements in Probabilistic
395(2)
11.4.6 A Brief Overview of Modeling Approaches: State of the Art
397(1)
11.4.7 A Simplified Approach to Digital System Modeling
398(13)
11.5 Codes and Standards for Digital Protection Systems
411(1)
11.6 Conclusions and Final Remarks
412(2)
References
414(3)
12 Physics-of-Failure Approach for Electronics
417(30)
12.1 Introduction
417(1)
12.2 Life Cycle Aspects and Failure Distributions
418(2)
12.3 Physics of Failure-Based Reliability
420(10)
12.3.1 Inputs for the PoF Approach
421(1)
12.3.2 Failure Modes, Mechanisms, and Effects Analysis (FMMEA)
422(8)
12.4 Virtual Qualification and Testing
430(3)
12.5 Physical Qualification
433(1)
12.6 System-Level Reliability and Standards
434(2)
12.7 Prognostics and Health Management
436(6)
12.7.1 Life Consumption Monitoring
436(2)
12.7.2 "Canary" Prognostics
438(4)
12.8 Conclusions
442(1)
References
443(4)
13 Prognostics and Health Management
447(62)
13.1 Introduction
447(3)
13.2 A Brief Overview of Surveillance and Condition Monitoring in NPPs
450(5)
13.3 Prognostics and Health Management and Integrated Risk-Based Engineering
455(1)
13.4 Requirements of Prognostics for IRBE Applications
456(6)
13.4.1 Plant Stage
457(1)
13.4.2 Objective
457(2)
13.4.3 State of the Art
459(1)
13.4.4 Application-Specific Prognostics
459(1)
13.4.5 Level of Implementation
459(1)
13.4.6 Risk Assessment Approach
460(1)
13.4.7 Existing Maintenance and Health Management Strategy
460(1)
13.4.8 Stakeholders
461(1)
13.4.9 Approach for Implementation
461(1)
13.4.10 Tools and Methods
462(1)
13.4.11 Cost/Benefit Studies
462(1)
13.5 Prognostic Framework for Nuclear Plants
462(37)
13.5.1 Existing Setup
462(2)
13.5.2 PHM Approaches
464(24)
13.5.3 Uncertainty in PHM
488(2)
13.5.4 Performance Metrics
490(7)
13.5.5 Verification and Validation of PHM Capabilities
497(1)
13.5.6 Limitations of Prognostic Methods
498(1)
13.6 Conclusions and Recommendations
499(2)
References
501(8)
14 Risk-Informed Decisions
509(22)
14.1 Introduction
509(3)
14.2 Generic Steps in Decision-Making
512(1)
14.3 Basic Requirements for Risk-Informed Decisions
513(1)
14.4 Role of PRA in the Risk-Informed Approach
514(1)
14.5 Acceptability of PRA as Part of Risk-Informed Applications
515(3)
14.5.1 Limitations of PRA
516(1)
14.5.2 PRA Requirements for Specific Applications
517(1)
14.6 Overview of Risk-Informed Developments
518(5)
14.6.1 IAEA RIDM Development Status
518(1)
14.6.2 USNRC Development Program
519(2)
14.6.3 NASA Risk-Informed Development Program
521(1)
14.6.4 NEA RIDM Development Status
522(1)
14.6.5 Related Literature on Risk-Informed Decision-Making
523(1)
14.7 Integrated Decision-Making
523(4)
14.8 Final Remarks and Conclusions
527(1)
References
528(3)
15 Risk-Based/Risk-Informed Applications
531(30)
15.1 Introduction
531(1)
15.2 Risk-Informed/Risk-Based Application Areas
531(1)
15.3 Risk-Informed/Risk-Based Case Studies
532(23)
15.3.1 Case Study 1: Integrated Design Evaluation
533(2)
15.3.2 Case Study 2: Surveillance Test Interval Optimization
535(2)
15.3.3 Case Study 3: Life Extension in Support of Relicensing
537(5)
15.3.4 Case Study 4: Risk Monitor
542(2)
15.3.5 Case Study 5: Risk-Based In-Service Inspection
544(4)
15.3.6 Case Study 6: Risk-Based Operator Support System
548(4)
15.3.7 Case Study 7: Risk-Based Approach to Re-assess the Enhancement in Safety Margin
552(3)
15.4 Concluding Remarks
555(3)
References
558(3)
Annexure 561
Prof. Prabhakar V. Varde is an expert in the field of application of reliability and probabilistic risk assessment to nuclear plants and is currently working as Head of the Research Reactor Services Division & Senior Professor at Homi Bhabha National Institute, Bhabha Atomic Research Centre, Mumbai (India), where he also serves in advisory and administrative capacities in Atomic Energy Regulatory Board (AERB), India and the Homi Bhabha National Institute, India. He is the founder and President of the Society for Reliability & Safety (SRESA), and is one of the Chief Editors for its international journal- Life Cycle Reliability and Safety Engineering. He completed his B.E. (Mech.) from Government Engineering College Rewa in 1983, and joined BARC, Mumbai, in 1984, where he as a Shift Engineer in the Reactor Operations Division until 1995. In 1996 he received his PhD in Reliability Engineer from the Indian Institute of Technology, Bombay, Mumbai, following which hehas worked as a post-doctoral fellow at the Korea Atomic Energy Research Institute, South Korea and a Visiting Professor, at Center for Advanced Life Cycle Engineering (CALCE) at University of Maryland, USA.

Prof Varde is also a consultant/specialist/Indian Expert for many international organizations, including the OECD/NEA (WGRISK) Paris, International Atomic Energy Agency, Vienna, University of Maryland, USA, Korea Atomic Energy Research Institute, South Korea, etc. Based on his research & development work, he has published over 200 publications in journals and conferences, including 11 conference proceedings books.

Prof Michael Pecht is a world-renowned expert in strategic planning, design, test, and risk assessment of electronics and information systems. Prof Pecht has a BS in Physics, an MS in Electrical Engineering and an MS and PhD in Engineering Mechanics from the University of Wisconsin at Madison. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow and an IMAPS Fellow. He is the editor-in-chief of IEEE Access, and served as chief editor of the IEEE Transactions on Reliability for nine years, and chief editor for Microelectronics Reliability for sixteen years. He has also served on three U.S. National Academy of Science studies, two US Congressional investigations in automotive safety, and as an expert to the U.S. Food and Drug Administration (FDA). He is the founder and Director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the worlds leading electronics companies at more than US$6M/year. The CALCE Center received the NSF Innovation Award in 2009 and the National Defense Industries Association Award. Prof Pecht is currently a Chair Professor in Mechanical Engineering and a Professor in Applied Mathematics, Statistics and Scientific Computation at the University of Maryland. He has written more than twenty books on product reliability, development, use and supply chain management. He has also written a series of books of the electronics industry in China, Korea, Japan and India. He has written over 700 technical articles and has 8 patents. In 2015 he was awarded the IEEE Components, Packaging, and Manufacturing Award for visionary leadership in the development of physics-of-failure-based and prognostics-based approaches to electronic packaging reliability. He was also awarded the Chinese Academy of Sciences Presidents International Fellowship. In 2013, he was awarded the University of Wisconsin-Madisons College of Engineering Distinguished Achievement Award. In 2011, he received the University of Marylands Innovation Award for his new concepts in risk management. In 2010, he received the IEEE Exceptional Technical Achievement Award for his innovations in the area of prognostics and systems health management. In 2008, he was awarded the highest reliability honor, the IEEE Reliability Societys Lifetime Achievement Award.