Advances in Reliability Science. Engineering Reliability And Risk Assessment

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Advances in Reliability Science: Engineering Reliability and Risk Assessment
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Contributors
1. Bayesian networks for failure analysis of complex systems using different data sources
	1. Introduction
	2. Risk, reliability, and uncertainty
	3. Bayesian networks (BNs)
	4. Probabilistic failure analysis of hydropower dams
	5. Summary and conclusions
	References
2. Failure modes and effect analysis model for the reliability and safety evaluation of a pressurized steam trap
	1. Introduction
	2. Hybrid failure modes and effects analysis model
		2.1 Complex intuitionistic fuzzy set (CIFS)
		2.2 Complex intuitionistic fuzzy Bonferroni mean (CIFBM) operator
		2.3 Complex intuitionistic Fuzzy-VIKOR model
		2.4 Algorithm of the hybrid failure modes and effects analysis model
	3. Numerical illustration
		3.1 Results and discussion
		3.2 Observation from the model implementation
	4. Conclusions
	References
3. Reliability and availability analysis of a standby system with activation time and varying demand
	Nomenclature
	1. Introduction
	2. Assumptions for proposed model
	3. Proposed system (model)
	4. Description of model
		4.1 Mean sojourn times and transition probabilities
		4.2 Mean time to system failure (MTSF)
		4.3 Availability analysis
			4.3.1 A single unit is operative
				4.3.1.1 Production made by a single unit is greater than demand
				4.3.1.2 Demand≥production by a single unit and “<” that by two units
				4.3.1.3 Production by two units is not greater than demand
			4.3.2 Two units are operative
				4.3.2.1 Demand≥production by a single unit and “<” that by two units
				4.3.2.2 Production by two units is not greater than demand
	5. Graphical interpretations
	6. Conclusions
	References
4. Fuzzy attack tree analysis of security threat assessment in an internet security system using algebraic t-norm and t-conorm
	1. Introduction
	2. Preliminary concepts
		2.1 Intuitionistic fuzzy set(IFS)
		2.2 Triangle intuitionistic fuzzy set(TIFS)
		2.3 Algebraic t-norm(TA) and t-conorm(SA)
		2.4 The fuzzy arithmetic operations defined on TIFS [29]
		2.5 Failure probability evaluation for OR and AND nodes [6,30]
	3. Proposed FATA method
	4. An illustrative application
		4.1 Results obtained from proposed FATA method
		4.2 Comparative analysis and discussion
	5. Conclusions and future scope
	References
5. A new flexible extension to a lifetime distributions, properties, inference, and applications in engineering science
	Symbols
	Abbreviations
	1. Introduction
	2. Special model
		2.1 The LE-inverse exponential (LE-IE) model
		2.2 Quantile function (QF)
	3. Reliability measures
		3.1 Failure function
		3.2 Reliability function
		3.3 Hazard function
		3.4 Mills ratio
		3.5 Cumulative hazard rate function
		3.6 Mean time to failure (MTTF) and mean time to repair (MTTR)
	4. Estimation inference via simulation
		4.1 Maximum likelihood estimation (MLE)
		4.2 Least square estimation (LSE)
		4.3 Simulation study
	5. Real data applications
	6. Conclusion
	References
6. Markov and semi-Markov models in system reliability
	1. The reliability in systems
	2. Failure process of systems
	3. Markov and semi-Markov models in systems reliability
	4. Conclusions and future research
	References
7. Emerging trends and future directions in software reliability growth modeling
	1. Introduction
	2. Software reliability growth models
		2.1 Nonhomogeneous poisson process
			2.1.1 Goel–Okumoto model (GO model)
		2.2 SRGMs development with various associated factors
			2.2.1 Perfect and imperfect debugging environment
			2.2.2 Fault detection rate
			2.2.3 SRGMs with environmental factors
	3. Method of model formulation
	4. Emerging trends
	5. Future direction
	6. Conclusions
	Acknowledgments
	References
8. Reliability and profit analysis of a markov model having cost-free warranty with waiting repair facility
	1. Introduction
	2. Background and literature review
		2.1 Concept of warranty
			2.1.1 Role of warranty
				2.1.1.1 Role to consumer/customer
				2.1.1.2 Role to manufacture
		2.2 Warranty cost analysis
		2.3 Shortcoming and overcoming of the literature
	3. Description of the system
		3.1 Assumptions
		3.2 State specifications
		3.3 Notations
	4. Analysis of the system
		4.1 Mathematical formulation of the model
		4.2 Solution of the equations
		4.3 Reliability of the system R(t) [29]
		4.4 Availability of the system Av (t)
		4.5 Busy period of the repairman BW period
		4.6 Profit analysis of the system
	5. Numerical results
		5.1 Interpretations of the numerical results
	6. Conclusion
	7. Future research directions
	Acknowledgment
	References
9. Semi-Markov modeling applications in system availability analysis
	1. System availability
	2. Motivation
	3. Availability assessment
	4. Availability assessment methods
		4.1 Markov method
		4.2 Semi-Markov method
	5. System availability modeling and analysis
		5.1 Steady-state solution
			5.1.1 Stage 1: EMC state probabilities
			5.1.2 Stage 2: SMP state probabilities
	6. Application of SMP for engineering systems
		6.1 Illustration 1: pumping system under preventive maintenance
			6.1.1 System availability modeling and analysis
		6.2 Vertical milling center under run-to-failure-maintenance
			6.2.1 System description
			6.2.2 Illustration
		6.3 Pumping system under condition-based maintenance
			6.3.1 System modeling with condition based maintenance
			6.3.2 Analytical solution
		6.4 Pumping system under opportunistic maintenance
			6.4.1 System modeling with opportunistic maintenance
			6.4.2 System model: planned perfect repair with opportunistic maintenance
	7. Conclusion
	References
10. An α-cut interval-based similarity aggregation method to evaluate fault tree events for system safety under fuzzy environment
	1. Introduction
	2. Preliminaries of fuzzy set theory
		2.1 Fuzzy set [28]
		2.2 Fuzzy number [28]
		2.3 α-Cut interval-based arithmetic operations on fuzzy numbers [28]
		2.4 Interval distance between fuzzy numbers [29]
		2.5 Interval distance-based similarity measure of fuzzy numbers [29]
		2.6 Linguistic terms [28]
		2.7 Defuzzification [31]
	3. Proposed approach
		3.1 Step 1: system identification and fault tree construction
		3.2 Step 2: determining BEs' possibilities through experts' judgments
		3.3 Step 3: generating fuzzy membership functions corresponding to linguistic terms
		3.4 Step 4: obtaining aggregated IVFPs of BEs using proposed approach
			3.4.1 Evaluating similarity degree between experts' judgments
			3.4.2 Average agreement degree evaluation
			3.4.3 Relative agreement degree evaluation
			3.4.4 Weighting factor calculation
			3.4.5 Obtaining aggregated weighting factor
			3.4.6 Generating aggregated IVFP of BEs
		3.5 Step 5: calculating crisp possibility score
		3.6 Step 6: evaluating the probabilities of BEs
		3.7 Step 7: generating top event probability
	4. Numerical example
	5. Conclusion
	References
11. Business analytics to advance industrial safety management
	1. Introduction
	2. The eMARS database
	3. Materials and method
	4. Results
		4.1 Industry type
		4.2 Hazardous materials
		4.3 Causes analysis
		4.4 Reporting motivation
	5. Conclusion
	References
12. Risk assessment and management of fire-induced domino effects in chemical industrial park
	1. Introduction
	2. Fire synergistic effect model (FSEM)
		2.1 Failure criterion of equipment
		2.2 Fire synergistic effect model
	3. Spatial–temporal evolution modeling of fire-induced domino effects based on FSEM
		3.1 Approach overview
		3.2 Approach procedures
		3.3 Model validation
	4. Risk management of fire-induced domino effects
		4.1 Approach overview
		4.2 Approach procedures
	5. Combining uncertainty reasoning and deterministic modeling
		5.1 Approach overview
		5.2 Approach procedures
	6. Conclusions
	References
13. Stability assessment using Bayesian network control for inverters in smart grid
	1. Introduction
	2. The TAN classifier and its AdaBoost algorithm
	3. The controller structure of dynamic Bayesian network–based model predictive control
		3.1 Model predictive control
		3.2 Dynamic Bayesian networks
	4. Controller of dynamic Bayesian network–based model predictive control for three-phase grid-connected inverter system
		4.1 Modeling of three-phase grid-connected inverter system
		4.2 Dynamic Bayesian networks for predictive modeling
		4.3 Optimization for generating the switching signals
	5. Experimentation and results
		5.1 Test scenario descriptions
		5.2 Steady-state performance study
		5.3 Dynamic state performance study
		5.4 The case study of new England IEEE 39-bus benchmark power system integrated with the battery energy storage system
		5.5 The robustness analysis of using the DBN-MPC method in the grid-connected inverter based power system
	6. Discussion and conclusion
	References
Index
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