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