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Submit a ProposalComputational Intelligence in Sustainable Reliability Engineering
 | Edited by S. C. Malik, Deepak Sinwar, Ashish Kumar, S. R. Gadde, Prasenjit Chatterjee and Bui Thanh Hung
Series: Sustainable Computing and Optimization Copyright: 2023 | Status: Published ISBN: 9781119865018 | Hardcover | 336 pages Price: $195 USD  |
One Line Description
The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering.
Audience
Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.
Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.
Description
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.
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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.
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Author / Editor Details
S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books.
S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books.
Deepak Sinwar, PhD, is an assistant professor in the Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India. His research interests include computational intelligence, data mining, machine learning, reliability theory, computer networks, and pattern recognition.
Ashish Kumar, PhD, is an assistant professor in the Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur. He has published more than 80 research papers in various national/international journals and participated in more than 50 conferences in India and abroad. His area of interest is reliability modeling and analysis, sampling theory, reliability estimation, and data analysis.
Gadde Srinivasa Rao, PhD, is a Professor of Statistics in the Department of Statistics, Dodoma University, Tanzania. He has published more than 140 articles in peer-reviewed journals and participated in more than 70 national and international conferences. His research interests include statistical inference, quality control, and reliability estimation.
Prasenjit Chatterjee, PhD, is the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 100 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 20 books and is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).
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Table of Contents
Preface
Acknowledgment
1. Reliability Indices of a Computer System with Priority and Server Failure
S.C. Malik, R.K. Yadav and N. Nandal
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
2. Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm
Monika Saini, Nivedita Gupta and Ashish Kumar
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
3. Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design
Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar
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
4. Applications of Cuckoo Search Algorithm in Reliability Optimization
V. Kaviyarasu and V. Suganthi
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
5. Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula
Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao
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
6. Applications of Artificial Intelligence in Sustainable Energy Development and Utilization
Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman
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
7. On New Joint Importance Measures for Multistate Reliability Systems
Chacko V. M.
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
8. Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data
Kapil Kumar and Anita Kumari
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
9. Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data
Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer
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
10. Cost-Benefit Analysis of a Redundant System with Refreshment
M.S. Barak and Dhiraj Yadav
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
11. Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making
Ram Naresh Saraswat and Sapna Gahlot
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
12. Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System
M.S. Barak, Ajay Kumar and Reena Garg
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
13. Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm
Monika Saini, Nivedita Gupta and Ashish Kumar
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
14. Investigation of Reliability and Maintainability of Piston Manufacturing Plant
Monika Saini, Deepak Sinwar and Ashish Kumar
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
Back to Top
Preface
Acknowledgment
1. Reliability Indices of a Computer System with Priority and Server Failure
S.C. Malik, R.K. Yadav and N. Nandal
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
2. Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm
Monika Saini, Nivedita Gupta and Ashish Kumar
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
3. Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design
Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar
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
4. Applications of Cuckoo Search Algorithm in Reliability Optimization
V. Kaviyarasu and V. Suganthi
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
5. Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula
Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao
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
6. Applications of Artificial Intelligence in Sustainable Energy Development and Utilization
Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman
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
7. On New Joint Importance Measures for Multistate Reliability Systems
Chacko V. M.
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
8. Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data
Kapil Kumar and Anita Kumari
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
9. Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data
Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer
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
10. Cost-Benefit Analysis of a Redundant System with Refreshment
M.S. Barak and Dhiraj Yadav
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
11. Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making
Ram Naresh Saraswat and Sapna Gahlot
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
12. Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System
M.S. Barak, Ajay Kumar and Reena Garg
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
13. Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm
Monika Saini, Nivedita Gupta and Ashish Kumar
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
14. Investigation of Reliability and Maintainability of Piston Manufacturing Plant
Monika Saini, Deepak Sinwar and Ashish Kumar
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
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