PRZEDMIOTEM OFERTY JEST KOD DOSTĘPOWY DO KSIĄŻKI ELEKTRONICZNEJ (EBOOK)
KSIĄŻKA JEST DOSTĘPNA NA ZEWNĘTRZNEJ PLATFORMIE. KSIĄŻKA NIE JEST W POSTACI PLIKU.
Reliability and safety are core issues that must be addressed throughout the life cycle of engineering systems. Reliability and Safety Engineering presents an overview of the basic concepts, together with simple and practical illustrations. The authors present reliability terminology in various engineering fields, viz., electronics engineering, software engineering, mechanical engineering, structural engineering, and power systems engineering. They describe the latest applications in the area of probabilistic safety assessment, such as technical specification optimization, risk monitoring and risk informed in-service inspection. Reliability and safety studies must, inevitably, deal with uncertainty, so the book includes uncertainty propagation methods: Monte Carlo simulation, fuzzy arithmetic, Dempster-Shafer theory and probability bounds. Reliability and Safety Engineering also highlights advances in system reliability and safety assessment including dynamic system modeling and uncertainty management. Case studies from typical nuclear power plants, as well as from structural, software, and electronic systems are also discussed. Reliability and Safety Engineering combines discussions of the existing literature on basic concepts and applications with state-of-the-art methods used in reliability and risk assessment of engineering systems. It is designed to assist practicing engineers, students and researchers in the areas of reliability engineering and risk analysis.
- Autorzy: Ajit Kumar Verma Srividya Ajit Durga Rao Karanki
- Wydawnictwo: Springer Nature
- Data wydania: 2010
- Wydanie:
- Liczba stron:
- Forma publikacji: PDF (online)
- Język publikacji: angielski
- ISBN: 9781849962322
BRAK MOŻLIWOŚCI POBRANIA PLIKU. Drukowanie: OGRANICZENIE DO 2 stron. Kopiowanie: OGRANICZENIE DO 2 stron.
- Foreword
- Preface
- Acknowledgments
- Contents
- 1 Introduction
- 1.1 Need for Reliability and Safety Engineering
- 1.2 Failures Inevitable
- 1.3 Improving Reliability and Safety
- 1.4 Definitions and Explanation of Some Relevant Terms
- 1.4.1 Quality
- 1.4.2 Reliability
- 1.4.3 Maintainability
- 1.4.3.1 Corrective Maintenance
- 1.4.3.2 Preventive Maintenance
- 1.4.3.3 Predictive Maintenance
- 1.4.4 Availability
- 1.4.5 Safety/Risk
- 1.4.6 Probabilistic Risk Assessment/Probabilistic Safety Assessment
- 1.5 Resources
- 1.6 History
- 1.7 Present Challenges and Future Needs for the Practice of Reliability and Safety Engineering
- References
- 2 Basic Reliability Mathematics
- 2.1 Classical Set Theory and Boolean Algebra
- 2.1.1 Operations on Sets
- 2.1.2 Laws of Set Theory
- 2.1.3 Boolean Algebra
- 2.2 Concepts of Probability Theory
- 2.2.1 Axioms of Probability
- 2.2.2 Calculus of Probability Theory
- 2.2.2.1 Independent Events and Mutually Exclusive Events
- 2.2.2.2 Conditional Probability
- 2.2.2.3 Probability for Intersection of Events
- 2.2.2.4 Probability for Union of Events
- 2.2.2.5 Total Probability Theorem
- 2.2.2.6 Bayes’ Theorem
- 2.2.3 Random Variables and Probability Distributions
- 2.2.3.1 Discrete Probability Distribution
- 2.2.3.2 Continuous Probability Distributions
- 2.2.3.3 Characteristics of Random Variables
- 2.3 Reliability and Hazard Functions
- 2.4 Distributions Used in Reliability and Safety Studies
- 2.4.1 Discrete Probability Distributions
- 2.4.1.1 Binomial Distribution
- 2.4.1.2 Poisson Distribution
- 2.4.1.3 Hypergeometric Distribution
- 2.4.1.4 Geometric Distribution
- 2.4.2 Continuous Probability Distributions
- 2.4.2.1 Exponential Distribution
- 2.4.2.2 Normal Distribution
- 2.4.2.3 Lognormal Distribution
- 2.4.2.4 Weibull Distribution
- 2.4.2.5 Gamma Distribution
- 2.4.2.6 Erlangian Distribution
- 2.4.2.7 Chi-square Distribution
- 2.4.2.8 F-distribution
- 2.4.2.9 t-distribution
- 2.4.3 Summary
- 2.5 Failure Data Analysis
- 2.5.1 Nonparametric Methods
- 2.5.2 Parametric Methods
- 2.5.2.1 Identifying Candidate Distributions
- 2.5.2.2 Estimating the Parameters of Distribution
- 2.5.2.3 Goodness-of-fit Tests
- Exercise Problems
- References
- 3 System Reliability Modeling
- 3.1 Reliability Block Diagram
- 3.1.1 Procedure for System Reliability Prediction Using Reliability Block Diagram
- 3.1.1.1 Important Points to be Considered while Constructing RBDs
- 3.1.2 Different Types of Models
- 3.1.2.1 Series Model
- 3.1.2.2 Parallel Model
- 3.1.2.3 M-out-of-N Models (Identical Items)
- 3.1.2.4 Standby Redundancy Models
- 3.1.3 Solving the Reliability Block Diagram
- 3.1.3.1 Truth Table Method
- 3.1.3.2 Cut-set and Tie-set Method
- 3.1.3.3 Bounds Method
- 3.2 Markov Models
- 3.2.1 State Space Method – Principles
- 3.2.1.1 Steps
- 3.2.1.2 Basic Analysis
- 3.2.1.3 State Frequencies and Durations
- 3.2.1.4 Two-component System with Repair
- 3.2.2 Safety Modeling
- 3.2.2.1 Imperfect Coverage – Two-component Parallel System
- 3.2.2.2 Modeling of Fault-tolerant Systems
- 3.3 Fault Tree Analysis
- 3.3.1 Procedure for Carrying out Fault Tree Analysis
- 3.3.1.1 System Awareness and Details
- 3.3.1.2 Defining Objectives, Top Event, and Scope of Fault Tree Analysis
- 3.3.1.3 Construction of the Fault Tree
- 3.3.1.4 Qualitative Evaluation of the Fault Tree
- 3.3.1.5 Data Assessment and Parameter Estimation
- 3.3.1.6 Quantitative Evaluation of the Fault Tree
- 3.3.1.7 Interpretation and Presentation of the Results
- 3.3.1.8 Important Points to Be Considered while Constructing Fault Trees
- 3.3.2 Elements of Fault Tree
- 3.3.3 Evaluation of Fault Tree
- 3.3.3.1 AND Gate
- 3.3.3.2 OR Gate
- 3.3.4 Case Study
- 3.3.4.1 Step 1 – Defining Top Event
- 3.3.4.2 Step 2 – Construction of the Fault Tree
- 3.3.4.3 Step 3 – Qualitative Evaluation
- 3.3.4.4 Step 4 – Quantitative Evaluation
- 3.4 Monte Carlo Simulation
- 3.4.1 Analytical versus Simulation Approaches for System Reliability Modeling
- 3.4.1.2 Benefits/Applications of Simulation-based Reliability Evaluation
- 3.4.2 Elements of Monte Carlo Simulation
- 3.4.3 Repairable Series and Parallel Systems
- 3.4.3.1 Reliability Evaluation with Analytical Approach
- 3.4.4 Simulation Procedure for Complex Systems
- 3.4.4.1 Case Study – AC Power Supply System of Indian Nuclear Power Plant
- 3.4.5 Increasing Efficiency of Simulation
- 3.4.5.1 Importance Sampling
- 3.4.5.2 Latin Hypercube Sampling
- 3.5 Dynamic Reliability Analysis
- 3.5.1 Dynamic Fault Tree Gates
- 3.5.1.1 PAND Gate
- 3.5.1.2 SEQ Gate
- 3.5.1.3 SPARE Gate
- 3.5.1.4 FDEP Gate
- 3.5.2 Modular Solution for Dynamic Fault Trees
- 3.5.3 Numerical Method
- 3.5.3.1 PAND Gate
- 3.5.3.2 SEQ Gate
- 3.5.3.3 SPARE Gate
- 3.5.4 Monte Carlo Simulation
- 3.5.4.1 PAND Gate
- 3.6.4.2 SPARE Gate
- 3.5.4.3 FDEP Gate
- 3.5.4.4 SEQ Gate
- 3.5.4.5 Case Study 1 – Simplified Electrical (AC) Power Supply System of Nuclear Power Plant
- 3.5.4.6 Case Study 2 – Reactor Regulation System of Nuclear Power Plant
- Exercise Problems
- References
- 4 Electronic System Reliability
- 4.1 Importance of Electronic Industry
- 4.2 Various Components Used and Their Failure Mechanisms
- 4.2.1 Resistors
- 4.2.2 Capacitors
- 4.2.3 Inductors
- 4.2.4 Relays
- 4.2.5 Semiconductor Devices
- 4.2.6 Integrated Circuits
- 4.3 Reliability Prediction of Electronic Systems
- 4.3.1 Part-count Method
- 4.3.2 Part-stress Method
- 4.4 PRISM
- 4.5 Sneak Circuit Analysis
- 4.5.1 Definition
- 4.5.2 Network Tree Production
- 4.5.3 Topological Pattern Identification
- 4.6 Case Study
- 4.6.1 Total Failure Rate
- 4.7 Physics of Failure Mechanisms of Electronic Components
- 4.7.1 Physics of Failures
- 4.7.2 Failure Mechanisms for Resistors
- 4.7.2.1 Failure Due to Excessive Heating
- 4.7.2.2 Failure Due to Metal Diffusion and Oxidation
- 4.7.3 Failure Mechanisms for Capacitors
- 4.7.3.1 Dielectric Breakdown
- 4.7.4 Failure Mechanisms for Metal Oxide Semiconductors
- 4.7.4.1 Electromigration
- 4.7.4.2 Time-dependent Dielectric Breakdown
- 4.7.4.3 Hot-carrier Injection
- 4.7.4.4 Negative Bias Temperature Instability
- 4.7.5 Field Programmable Gate Array
- 4.7.5.1 Hierarchical Model
- 4.7.5.2 Optimal Model
- 4.7.5.3 Coarse Model
- 4.7.5.4 Tile-based Model
- References
- 5 Software Reliability
- 5.1 Introduction to Software Reliability
- 5.2 Past Incidences of Software Failures in Safety Critical Systems
- 5.2.1 Therac-25 Failure
- 5.2.2 Ariane 5 Failure
- 5.2.3 Patriot Failure
- 5.3 The Need for Reliable Software
- 5.4 Difference Between Hardware Reliability and Software Reliability
- 5.5 Software Reliability Modeling
- 5.5.1 Software Reliability Growth Models
- 5.5.2 Black-box Software Reliability Models
- 5.5.3 White-box Software Reliability Models
- 5.6 How to Implement Software Reliability
- 5.6.1 Example – Operational Profile Model
- 5.6.2 Case Study
- 5.6.2.1 Step 1 – Determine All Possible Modules, Submodules and Scenarios
- 5.6.2.2 Step 2 – Create n × n Matrix
- 5.6.2.3 Step 3 – Add the Possible Scenarios from n × n Matrix to the List of Scenarios
- 5.6.2.4 Step 4 – Assign Probability of Modules
- 5.6.2.5 Step 5 – Assign Probability of Submodules
- 5.6.2.6 Step 6 – Assign Probability of Scenarios
- 5.6.2.7 Step 7 – Generate Random Numbers
- 5.6.3 Benefits
- 5.7 Emerging Techniques in Software Reliability Modeling – Soft Computing Technique
- 5.7.1 Need for Soft Computing Methods
- 5.7.2 Environmental Parameters
- 5.7.2.1 Defect Rating
- 5.7.2.2 Project Risk Index
- 5.7.2.3 Process Compliance Index
- 5.7.2.4 Group Maturity Rating
- 5.7.3 Anil–Verma Model
- 5.7.3.1 Results Obtained from Anil–Verma Model
- 5.7.3.2 Implementation Guidelines for Anil–Verma Model
- 5.8 Future Trends of Software Reliability
- References
- 6 Mechanical Reliability
- 6.1 Reliability versus Durability
- 6.2 Failure Modes in Mechanical Systems
- 6.2.1 Failures Due to Operating Load
- 6.2.2 Failures Due to Environment
- 6.2.3 Failures Due to Poor Manufacturing Quality
- 6.3 Reliability Circle
- 6.3.1 Specify Reliability
- 6.3.1.1 Quality Function Deployment – Capturing the Voice of the Customer
- 6.3.1.2 Reliability Measures
- 6.3.1.3 Environment and Usage
- 6.3.1.4 Reliability Apportionment
- 6.3.2 Design for Reliability
- 6.3.2.1 Reliability Analysis and Prediction
- 6.3.2.2 Stress-Strength Interference Theory
- 6.3.3 Test for Reliability
- 6.3.3.1 Reliability Test Objectives
- 6.3.3.2 Types of Testing
- 6.3.3.3 Reliability Test Program
- 6.3.3.4 Degradation Data Analysis
- 6.3.4 Maintain Manufacturing Reliability
- 6.3.4.1 Process Control Methods
- 6.3.4.2 Online Quality Control
- 6.3.5 Operational Reliability
- 6.3.5.1 Weibull Analysis
- References
- 7 Structural Reliability
- 7.1 Deterministic versus Probabilistic Approach in Structural Engineering
- 7.2 The Basic Reliability Problem
- 7.2.1 First-order Second-moment Method
- 7.2.2 Advanced First-order Second-moment Method
- 7.3 First-order Reliability Method
- 7.4 Reliability Analysis for Correlated Variables
- 7.4.1 Reliability Analysis for Correlated Normal Variables
- 7.4.2 Reliability Analysis for Correlated Non-normal Variables
- 7.4.2.1 Rosenblatt Transformation
- 7.4.2.2 Nataf Transformation
- 7.5 Second-order Reliability Methods
- 7.6 System Reliability
- 7.6.1 Classification of Systems
- 7.6.1.1 Series System
- 7.6.1.2 Parallel System
- 7.6.1.3 Combined Series–Parallel Systems
- 7.6.2 Evaluation of System Reliability
- 7.6.2.1 Numerical Integration
- 7.6.2.2 Bounding Techniques
- 7.6.2.3 Approximate Methods
- References
- 8 Power System Reliability
- 8.1 Introduction
- 8.2 Basics of Power System Reliability
- 8.2.1 Functional Zones and Hierarchical Levels
- 8.2.2 Adequacy Evaluation in Hierarchical Level I Studies
- 8.2.2.1 Construction of Capacity Outage Probability Table
- 8.2.2.2 Loss of Load Probability and Expected Energy Not Supplied
- 8.2.3 Adequacy Evaluation in Hierarchical Level II Studies
- 8.2.3.1 Basic Adequacy Indices
- 8.2.3.2 IEEE Proposed Adequacy Indices
- 8.2.4 Distribution System Reliability
- 8.3 Reliability Test Systems
- 8.4 Advances in Power System Reliability – Power System Reliability in the Deregulated Scenario
- References
- 9 Probabilistic Safety Assessment
- 9.1 Introduction
- 9.2 Concept of Risk and Safety
- 9.3 Probabilistic Safety Assessment Procedure
- 9.4 Identification of Hazards and Initiating Events
- 9.4.1 Preliminary Hazard Analysis
- 9.4.2 Master Logic Diagram
- 9.5 Event Tree Analysis
- 9.5.1 Procedure for Event Tree Analysis
- 9.6 Importance Measures
- 9.6.1 Birnbaum Importance
- 9.6.2 Inspection Importance
- 9.6.3 Fussell–Vesely Importance
- 9.7 Common-cause Failure Analysis
- 9.7.1 Treatment of Dependent Failures
- 9.7.1.1 Functional Dependences
- 9.7.1.2 Physical Dependences
- 9.7.1.3 Human Interaction Dependence
- 9.7.1.4 Defense Against Common-cause Failure
- 9.7.2 Procedural Framework for Common-cause Failure Analysis
- 9.7.3 Treatment of Common-cause Failures in Fault Tree Models
- 9.7.4 Common-cause Failure Models
- 9.7.4.1 Non-shock Models
- 9.7.4.2 Shock Models
- 9.8 Human Reliability Analysis
- 9.8.1 Human Behavior and Errors
- 9.8.2 Categorization of Human Interactions in Probabilistic Safety Assessment
- 9.8.2.1 Category A: Pre-initiators
- 9.8.2.2 Category B: Initiators
- 9.8.2.3 Category C: Post-initiators
- 9.8.3 Steps in Human Reliability Analysis
- 9.8.3.1 Definition
- 9.8.3.2 Screening
- 9.8.3.3 Qualitative Analysis
- 9.8.3.4 Representation and Model Integration
- 9.8.3.5 Quantification
- References
- 10 Applications of Probabilistic Safety Assessment
- 10.1 Objectives of Probabilistic Safety Assessment
- 10.2 Probabilistic Safety Assessment of Nuclear Power Plants
- 10.2.1 Description of Pressurized Heavy-water Reactors
- 10.2.1.1 Reactor Process System
- 10.2.1.2 Reactor Protection System
- 10.2.1.3 Electrical Power System
- 10.2.2 Probabilistic Safety Assessment of Indian Nuclear Power Plants (Pressurized Heavy-water React
- 10.2.2.1 Dominating Initiating Events
- 10.2.2.2 Reliability Analysis
- 10.2.2.3 Accident Sequence Identification
- 10.2.2.4 Event Trees
- 10.2.2.5 Dominating Accident Sequences
- 10.2.2.6 Risk Importance Measures
- 10.3 Technical Specification Optimization
- 10.3.1 Traditional Approaches for Technical Specification Optimization
- 10.3.1.1 Measures Applicable for Allowed Outage Time Evaluations
- 10.3.1.2 Measures Applicable for Surveillance Test Interval Evaluations
- 10.3.2 Advanced Techniques for Technical Specification Optimization
- 10.3.2.1 Mathematical Modeling of Problem
- 10.3.2.2 Genetic Algorithm as Optimization Method
- 10.3.2.3 Case Studies: Test Interval Optimization for Emergency Core Cooling System of Pressurized H
- 10.4 Risk Monitor
- 10.4.1 Necessity of Risk Monitor?
- 10.4.2 Different Modules of Risk Monitor
- 10.4.3 Applications of Risk Monitor
- 10.4.3.1 Decision-making in Operations
- 10.4.3.2 Maintenance Strategies
- 10.4.3.3 Risk-based In-Service Inspection
- 10.4.3.4 Incident Severity Assessment
- 10.4.3.5 Review of Technical Specification
- 10.4.3.6 Emergency Operating Procedures and Risk Management
- 10.5 Risk-informed In-service Inspection
- 10.5.1 Risk-informed In-service Inspection Models
- 10.5.1.1 American Society of Mechanical Engineers/Westinghouse Owners Group Model
- 10.5.1.2 Electric Power Research Institute Model
- 10.5.1.3 Comparison of Risk-informed In-service Inspection Models
- 10.5.2 In-service Inspection and Piping Failure Frequency
- 10.5.2.1 In-service Inspection
- 10.5.2.2 Models for Including In-service Inspection Effect on Piping Failure Frequency
- 10.5.3 Case Study
- 10.5.3.1 Assumptions
- 10.5.3.2 Consequence Analysis of Feeder Failure
- 10.5.3.3 Using the Three-state Markov Model
- 10.5.3.4 Using the Four-state Markov Model
- 10.5.4 Remarks on Risk-informed In-service Inspection
- References
- 11 Uncertainty Managementin Reliability/Safety Assessment
- 11.1 Mathematical Models and Uncertainties
- 11.1.1 Example for Understanding of Epistemic and Aleatory Uncertainties
- 11.2 Uncertainty Analysis: an Important Task of Probabilistic Risk/Safety Assessment
- 11.3 Methods of Characterizing Uncertainties
- 11.3.1 The Probabilistic Approach
- 11.3.2 Interval and Fuzzy Representation
- 11.3.2.1 Interval Representation
- 11.3.2.2 Fuzzy Representation
- 11.3.3 Dempster–Shafer-theory-based Representation
- 11.3.3.1 Frame of Discernment – X or ?
- 11.3.3.2 Basic Belief Assignment
- 11.3.3.3 Belief and Plausibility Functions
- 11.4 Uncertainty Propagation
- 11.4.1 Method of Moments
- 11.4.1.1 Approximation from the Taylor Series
- 11.4.1.2 Consideration of Correlation Using Method of Moments
- 11.4.2 Monte Carlo Simulation
- 11.4.2.1 Crude Monte Carlo Sampling
- 11.4.2.2 Latin Hypercube Sampling
- 11.4.3 Interval Arithmetic
- 11.4.4 Fuzzy Arithmetic
- 11.4.4.1 Probability to Possibility Transformations
- 11.5 Uncertainty Importance Measures
- 11.5.1 Probabilistic Approach to Ranking Uncertain Parameters in System Reliability Models
- 11.5.1.1 Correlation Coefficient Method
- 11.5.1.2 Variance-based Method
- 11.5.2 Method Based on Fuzzy Set Theory
- 11.5.3 Application to a Practical System
- 11.6 Treatment of Aleatory and Epistemic Uncertainties
- 11.6.1 Epistemic and Aleatory Uncertainty in Reliability Calculations
- 11.6.2 Need to Separate Epistemic and Aleatory Uncertainties
- 11.6.3 Methodology for Uncertainty Analysis in Reliability Assessment Based on Monte Carlo Simulatio
- 11.6.3.1 Methodology
- 11.7 Dempster–Shafer Theory
- 11.7.1 Belief and Plausibility Function of Real Numbers
- 11.7.2 Dempster’s Rule of Combination
- 11.7.3 Sampling Technique for the Evidence Theory
- 11.8 Probability Bounds Approach
- 11.8.1 Computing with Probability Bounds
- 11.8.1.1 Basic Calculations for Construction of P-box
- 11.8.2 Two-phase Monte Carlo Simulation
- 11.8.3 Uncertainty Propagation Considering Correlation Between Variables
- 11.9 Bayesian Approach
- 11.9.1 Bayes’ Theorem
- 11.9.2 Identification of Parameter
- 11.9.3 Development of Prior Distribution
- 11.9.4 Construction of Likelihood Function
- 11.9.5 Derivation of Posterior Distribution
- 11.9.6 Characteristic Parameters of Posterior Distribution
- 11.9.7 Estimation of Parameters from Multiple Sources of Information
- 11.9.8 The Hierarchical Bayes Method
- 11.10 Expert Elicitation Methods
- 11.10.1 Definition and Uses of Expert Elicitation
- 11.10.2 Treatment of Expert Elicitation Process
- 11.10.3 Methods of Treatment
- 11.10.3.1 Indirect Elicitation Method
- 11.10.3.2 Direct Elicitation Methods
- 11.10.3.3 Geometric Averaging Technique
- 11.10.3.4 Percentiles for Combining Expert Opinions
- 11.11 Case Study to Compare Uncertainty Analysis Methods
- 11.11.1 Availability Assessment of Main Control Power Supply Using Fault Tree Analysis
- 11.11.2 Uncertainty Propagation in Main Control Power Supply with Different Methods
- 11.11.2.1 Interval Analysis
- 11.11.2.2 Fuzzy Arithmetic
- 11.11.2.3 Monte Carlo Simulation
- 11.11.2.4 Dempster–Shafer Theory
- 11.11.2.5 Probability Bounds Analysis
- 11.11.3 Observations from Case Study
- 11.11.3.1 Remarks
- Exercise Problems
- References
- Appendix: Distribution Tables
- Index
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