Advances in Reliability, Failure and Risk Analysis

Preface
Contents
Editor and Contributors
1 Degradation and Failure Mechanisms of Complex Systems: Principles
1.1 Introduction
1.2 Mechanical Degradation and Failure Mechanisms
1.2.1 Wear-Out Mechanisms
1.2.2 Overstress Mechanisms
1.3 Thermal Degradation and Failure Mechanisms
1.3.1 Wear-Out Mechanisms
1.3.2 Overstress Mechanisms
1.4 Chemical Degradation and Failure Mechanisms
1.4.1 Wear-Out Mechanisms
1.4.2 Overstress Mechanisms
1.5 Electronics Degradation and Failure Mechanisms
1.5.1 Dielectric Breakdown
1.5.2 Bias Temperature Instability (BTI)
1.5.3 Hot Carrier Injection (HCI)
1.5.4 Electromagnetic Interference (EMI)
1.5.5 Electrostatic Discharge (ESD)
1.5.6 Electrical Overstress (EOS)
1.5.7 Electromigration (EM)
1.5.8 Tin Whiskers
1.5.9 Self-healing Accumulation
1.5.10 Self-heating
1.5.11 Electrochemical Corrosion
1.5.12 Thermal Overstress Induced Electrolyte Evaporation
1.6 Radiation Degradation and Failure Mechanisms
1.6.1 Lattice Displacement
1.6.2 Ionization Effects
1.7 Human Failure Modes
1.7.1 Human Error
1.7.2 Violation
1.8 Software Errors and Failure Mechanisms
1.9 Cyber–Physical–Human (CPH) Systems’ Interaction Failure Mechanisms
1.10 Discussion and Conclusion
References
2 Simplified Approach to Analyse the Fuzzy Reliability of a Repairable System
2.1 Introduction
2.2 Preliminary Concepts
2.2.1 Fuzzy Set ch2zadeh1965
2.2.2 TFN ch2chen1994
2.2.3 Simplified Fuzzy Arithmetic Operations for TFNs ch2chen1994
2.3 The LT, FLT, and Proposed SFLT Techniques
2.3.1 LT Method ch2kbmishra
2.3.2 FLT Technique ch2knezevic
2.3.3 The Proposed SFLT Technique
2.4 An Illustration
2.4.1 A Brief Overview of the System
2.4.2 System Fuzzy Reliability Assessment Using SFLT Technique
2.4.3 Comparative Analysis
2.4.4 System Fuzzy Reliability Estimation for Long-Term Period
2.4.5 Sensitivity Analysis
2.4.6 Ranking of System Critical Components
2.5 Conclusion
References
3 Bayesian Reliability Analysis of Topp-Leone Model Under Different Loss Functions
3.1 Topp-Leone Distribution Model
3.2 Bayesian Estimation of Parameters of T-L Distribution with Complete Sample
3.2.1 Bayesian Estimation Under Quasi-prior Distribution
3.2.2 Comparative Study of Risk Functions for These Bayesian Estimators
3.3 Bayesian Reliability Analysis of T-L Distribution Based on Record Values
3.3.1 Record Values and Compound LINEX Loss Function
3.3.2 ML and Minimum Variance Unbiased Estimation
3.3.3 Bayesian Estimation Under CLL Function
3.4 Conclusions
References
4 Reliability Metrics of Textile Confection Plant Using Copula Linguistic
4.1 Introduction
4.2 Abbreviations, Description, and State of the Confection Plant
4.2.1 Abbreviations
4.2.2 The Description of the Confection Plant
4.2.3 Description of the State
4.3 Formulation of Textile Confection Plant Mathematical Model
4.3.1 Mathematical Model of Textile Confection Plant Solution
4.4 Investigation of Textile Confection Plant Model for Numerous Occurrences
4.4.1 Analysis of Availability
4.4.2 Analysis of Reliability
4.4.3 Analysis of MTTF
4.4.4 Analysis of Sensitivity
4.4.5 Analysis of Cost
4.5 Discussion and Concluding Remark
References
5 An Application of Soft Computing in Oil Condition Monitoring
5.1 Introduction
5.2 Importance of Oil Condition Monitoring in Agro-industries
5.3 Implementation of Oil Condition Monitoring
5.3.1 Fuzzy Systems
5.4 Analysis of Oil Condition Monitoring
5.5 Conclusion
References
6 A Multi-parameter Occupational Safety Risk Assessment Model for Chemicals in the University Laboratories by an MCDM Sorting Method
6.1 Introduction
6.2 Literature Review
6.2.1 Past Studies Carried Out for University Laboratory Safety
6.2.2 Past Studies Carried Out on Occupational Safety Risk Assessment via MCDM Sorting
6.3 The Proposed Methodology
6.3.1 Establishing the Multi-parameter Occupational Safety Risk Assessment
6.3.2 Determining the Weight of Risk Parameters via BWM
6.3.3 Calculating the Risk Priority Classes of Chemicals in the Lab via TOPSIS-Sort
6.3.4 Suggesting Control Measures
6.4 Case Study
6.4.1 System Environment of the University Chemical Laboratory and Chemical List
6.4.2 The Exploitation of BWM in the Determination of Risk Parameter Weights
6.4.3 The Exploitation of TOPSIS-Sort in Risk Classification of Chemicals
6.4.4 Risk Management of the Laboratory
6.5 Conclusion
References
7 Smart Failure Mode and Effects Analysis (FMEA) for Safety–Critical Systems in the Context of Industry 4.0
7.1 Introduction
7.1.1 Types of FMEA
7.2 FMEA Methodology
7.2.1 Classical-FMEA
7.2.2 Hybrid-FMEA Model
7.3 FMEA for Safety–Critical Systems
7.3.1 Basic Concept and Definition
7.3.2 Functional Safety Standards
7.3.3 Safety Barrier and Life Cycle
7.3.4 FMEA Implementation: Automotive Safety–Critical Systems
7.4 Smart-FMEA Applied for Asset Digital Transformation
7.5 Conclusion
References
8 Optimization of Redundancy Allocation Problem Using Quantum Particle Swarm Optimization Algorithm Under Uncertain Environment
8.1 Introduction
8.2 Assumptions and Notation
8.2.1 Assumptions
8.2.2 Notation
8.3 Some Definitions
8.3.1 Fuzzy Number
8.3.2 Trapezoidal Fuzzy Number (TrFN)
8.3.3 Beta and Uniform Distribution Method of Crispification
8.4 Formulation of the Problem
8.5 Solution Methodology
8.6 Quantum Particle Swarm Optimization (QPSO)
8.7 Integration Handling Technique
8.8 Numerical Example
8.9 Result Analysis
8.10 Conclusions and Future Scopes
References
9 Resilience: Business Sustainability Based on Risk Management
9.1 Introduction
9.2 Definition of Resilience, Robustness, and Antifragility
9.2.1 Different Levels of Resilience
9.3 Risk, Risk Management, and Business Resilience
9.4 Conclusion and Discussion
References
10 Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory
10.1 Introduction
10.2 Background
10.2.1 Uncertainty Sources in Chemical Process Industries
10.2.2 IFS Theory
10.3 Material and Method
10.3.1 Hazard Analysis
10.3.2 Developing a Fault Tree and Collecting Data
10.3.3 Use of the Expert System
10.3.4 Calculation of Probability of TE
10.3.5 Different Approach Comparison
10.3.6 Sensitivity Analysis
10.4 Application to the Case Study
10.4.1 Probabilistic Risk Assessment
10.4.2 Sensitivity Analysis
10.4.3 Identification of Critical BEs and Corrective Actions for the Most Critical BEs
10.5 Conclusion
References
11 Smart Systems Risk Management in IoT-Based Supply Chain
11.1 Introduction
11.2 IoT-Based Supply Chain
11.3 Internet of Things and Risks
11.4 Risk and Cybersecurity Management Process
11.5 Smart Systems Risk Management in IoT-Based Supply Chain
11.6 Quantitative Assessment of IoT-Based Supply Chain Risks
11.6.1 Mikhailov Ranking Method
11.6.2 Research Findings
11.7 Conclusion
References
12 Risk and Reliability Analysis in the Era of Digital Transformation
12.1 Introduction
12.2 Reliability Analysis
12.2.1 Big Data and Data Processing
12.2.2 Internet of Things
12.2.3 Cyber-Physical System
12.3 Assessment of Safety Risks
12.3.1 Big Data
12.3.2 Cyber-Physical System
12.4 Conclusion
References
13 Qualitative Analysis Method for Evaluation of Risk and Failures in Wind Power Plants: A Case Study of Turkey
13.1 Introduction
13.2 Literature Review
13.2.1 Risk Management in the Renewable Energy
13.2.2 Risk Management in Wind Power Plants
13.2.3 Risk Management Methods in Related Literature
13.3 Methodology
13.4 Case Scenario: Evaluation of Wind Power Plants in Turkey
13.5 Conclusion and Future Research
References
14 Some Discrete Parametric Markov–Chain System Models to Analyze Reliability
14.1 Introduction
14.2 Concepts Used in Analyzing System Models
14.3 Concept of Discrete Failure and Repair Time Models
14.4 Development of Some Important Results
14.5 Analysis of n-Unit Series System
14.6 Analysis of n-Unit Parallel System
14.7 Analysis of n-Unit Standby System
14.8 A Two Identical Unit Warm Standby System Model with Geometric Failure and Repair Time Distributions
14.8.1 Transition Probabilities
14.8.2 Mean Sojourn Time in the Various States
14.8.3 Analysis of Reliability and MTSF
14.8.4 Availability Analysis
14.8.5 Busy Period Analysis
14.8.6 Profit Function Analysis
14.8.7 Graphical Conclusions
References
15 Distributed System Reliability Analysis with Two Coverage Factors: A Copula Approach
15.1 Introduction
15.2 Literature Review
15.3 Nomenclatures and Model Description
15.3.1 Nomenclatures
15.3.2 Model Description
15.3.3 Solution of the Model
15.4 Analytical Analysis of the Model for Particular Cases
15.4.1 Availability Analysis
15.4.2 Reliability Analysis
15.4.3 Cost Analysis
15.5 Results Discussion
15.6 Conclusion
References
16 Repair and Maintenance Management System of Food Processing Equipment
16.1 Introduction
16.2 RAM Theory
16.2.1 Reliability
16.2.2 Availability
16.2.3 Maintainability
16.3 Application of RAM Analysis in the Food Processing Lines
16.3.1 Juice Bottling
16.3.2 Canned Products
16.3.3 Dairy Products
16.3.4 Milling Process
16.4 Conclusions
References
17 Reliability, Availability, Maintainability, and Dependability of a Serial Rice Mill Plant (RMP) with the Incorporation of Coverage Factor
17.1 Introduction
17.2 Materials and Method
17.2.1 Reliability Function
17.2.2 Availability Function
17.2.3 Maintainability
17.2.4 Dependability
17.2.5 MTBF
17.2.6 MTTR
17.2.7 Exponential Distribution
17.2.8 Constant Failure Rate
17.2.9 Notations
17.3 System Description
17.3.1 Description
17.3.2 Objectives
17.3.3 Assumption
17.4 RAMD Analysis of the System
17.4.1 RAMD Indices for Subsystem A
17.4.2 RAMD Indices for Subsystem B
17.4.3 RAMD Indices for Subsystem C
17.5 Numerical Simulation
17.6 Result Discussion
17.7 Conclusion
References