Computational Intelligence in Sustainable Reliability Engineering

Description

COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERING The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering. This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations. Audience Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design. Cover Title Page Copyright Page Contents Preface Acknowledgment Chapter 1 Reliability Indices of a Computer System with Priority and Server Failure 1.1 Introduction 1.2 Some Fundamentals 1.2.1 Reliability 1.2.2 Mean Time to System Failure (MTSF) 1.2.3 Steady State Availability 1.2.4 Redundancy 1.2.5 Semi-Markov Process 1.2.6 Regenerative Point Process 1.3 Notations and Abbreviations 1.4 Assumptions and State Descriptions 1.5 Reliability Measures 1.5.1 Transition Probabilities 1.5.2 MST 1.5.3 Reliability and MTCSF 1.5.4 Availability 1.5.5 Expected Number of Hardware Repairs 1.5.6 Expected Number of Software Upgradations 1.5.7 Expected Number of Treatments Given to the Server 1.5.8 Busy Period of Server Due to H/w Repair 1.5.9 Busy Period of Server Due to Software Upgradation 1.6 Profit Analysis 1.7 Particular Case 1.8 Graphical Presentation of Reliability Indices 1.9 Real-Life Application 1.10 Conclusion References Chapter 2 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 2.1 Introduction 2.2 System Description, Notations, and Assumptions 2.2.1 System Description 2.2.2 Notations 2.2.3 Assumptions 2.3 Mathematical Modeling of the System 2.4 Optimization 2.4.1 Genetic Algorithm 2.5 Results and Discussion 2.6 Conclusion References Chapter 3 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 3.1 Introduction 3.2 Problem Formulation of Harmonics 3.3 Development of Laplacian Artificial Bee Colony Algorithm 3.3.1 Basic Concepts of ABC 3.3.2 The Proposed LABC Algorithm 3.4 Discussion 3.5 Numerical Validation of Proposed Variant 3.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 3.5.2 Benchmark Test on CEC-17 Functions 3.6 Analytical Validation of Proposed Variant 3.6.1 Convergence Rate Test 3.6.2 Box Plot Analysis 3.6.3 Wilcoxon Rank Sum Test 3.6.4 Scalability Test 3.7 Design Analysis of Harmonic Estimator 3.7.1 Assessment of Harmonic Estimator Design Problem 1 3.7.2 Assessment of Harmonic Estimator Design Problem 2 3.8 Conclusion References Chapter 4 Applications of Cuckoo Search Algorithm in Reliability Optimization 4.1 Introduction 4.2 Cuckoo Search Algorithm 4.2.1 Performance of Cuckoo Search Algorithm 4.2.2 Levy Flights 4.2.3 Software Reliability 4.3 Modified Cuckoo Search Algorithm (MCS) 4.4 Optimization in Module Design 4.5 Optimization at Dynamic Implementation 4.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 4.7 Results and Discussions 4.8 Conclusion References Chapter 5 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 5.1 Introduction 5.2 Assumptions, Notations, and Description of the System 5.2.1 Notations 5.2.2 Assumptions 5.2.3 Description of the System 5.3 Reliability Formulation of Models 5.3.1 Solution of the Model 5.4 Some Particular Cases Based on Analytical Analysis of the Model 5.4.1 Availability Analysis 5.4.2 Reliability Analysis 5.4.3 Mean Time to Failure (MTTF) 5.4.4 Cost-Benefit Analysis 5.5 Conclusions Through Result Discussion References Chapter 6 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 6.1 Energy and Environment 6.2 Sustainable Energy 6.3 Artificial Intelligence in Industry 4.0 6.4 Introduction to AI and its Working Mechanism 6.5 Biodiesel 6.6 Transesterification Process 6.7 AI in Biodiesel Applications 6.8 Conclusion References Chapter 7 On New Joint Importance Measures for Multistate Reliability Systems 7.1 Introduction 7.2 New Joint Importance Measures 7.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 7.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 7.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 7.3 Discussion 7.4 Illustrative Example 7.5 Conclusion References Chapter 8 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 8.1 Introduction 8.2 Model Description 8.3 Classical Estimation 8.3.1 Maximum Likelihood Estimation 8.3.2 Asymptotic Confidence Interval 8.4 Bayesian Estimation 8.4.1 Tierney-Kadane’s Approximation 8.4.2 Metropolis-Hastings Algorithm 8.4.3 HPD Credible Interval 8.5 Simulation Study 8.6 Real-Life Application 8.7 Conclusions References Chapter 9 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 9.1 Introduction 9.2 Fuzzy Lifetime Data 9.2.1 Fuzzy Set 9.2.2 Fuzzy Numbers and Membership Function 9.2.3 Fuzzy Event and its Probability 9.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 9.4 Maximum Likelihood Estimation with Exponential Lifetimes 9.4.1 Bootstrap Confidence Interval 9.5 Bayes Estimation 9.5.1 Highest Posterior Density Confidence Estimates 9.6 Numerical Illustration 9.6.1 Simulation Study 9.6.2 Reliability Analysis Using Simulated Data 9.7 Real Data Study 9.8 Conclusion References Chapter 10 Cost-Benefit Analysis of a Redundant System with Refreshment 10.1 Introduction 10.2 Notations 10.3 Average Sojourn Times and Probabilities of Transition States 10.4 Mean Time to Failure of the System 10.5 Steady-State Availability 10.6 The Period in Which the Server is Busy With Inspection 10.7 Expected Number of Visits for Repair 10.8 Expected Number of Refreshments 10.9 Particular Case 10.10 Cost-Benefit Examination 10.11 Discussion 10.12 Conclusion References Chapter 11 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 11.1 Introduction 11.2 New f-Divergence Measure on Fuzzy Sets 11.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 11.4 Applications for Some Fuzzy f-Divergence Measures 11.5 Applications in MCDM 11.5.1 Case Study 11.6 Conclusion References Chapter 12 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 12.1 Introduction 12.2 The Assumptions and Notations Used to Solve the System 12.3 The Probabilities of States Transitions 12.4 Mean Sojourn Time 12.5 Mean Time to Failure of the System 12.6 Steady-State Availability 12.7 Busy Period of the Server Due to Repair of the Failed Unit 12.8 Busy Period of the Server Due to Refreshment 12.9 Estimated Visits Made by the Server 12.10 Particular Cases 12.11 Profit Analysis 12.12 Discussion 12.13 Conclusion 12.14 Contribution of Refreshment 12.15 Future Scope References Chapter 13 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 13.1 Introduction 13.2 System Description, Notations, and Assumptions 13.2.1 System Description 13.2.2 Notations 13.2.3 Assumptions 13.3 Mathematical Modeling of the System 13.4 Availability Optimization of Proposed Model 13.5 Results and Discussion 13.6 Conclusion References Chapter 14 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 14.1 Introduction 14.2 System Description and Data Collection 14.3 Descriptive Analysis 14.4 Power Law Process Model 14.5 Trend and Serial Correlation Analysis 14.6 Reliability and Maintainability Analysis 14.7 Conclusion References Index EULA