Engineering reliability and risk assessment /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Garg, Harish (Editor ), Ram, Mangey (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier, 2022.
Colección:Advances in reliability science
Temas:
Reliability (Engineering)
Risk management.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Engineering Reliability and Risk Assessment
  • Advances in Reliability Science: Engineering Reliability and Risk Assessment
  • Copyright
  • CONTENTS
  • 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 Demandeproduction by a single unit and "&lt
  • " 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 Demandeproduction by a single unit and "&lt
  • " 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 ...
  • 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 sc ...
  • 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.
  • 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
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • Back Cover.

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