Computational Intelligence in Sustainable Reliability Engineering

Computational Intelligence in Sustainable Reliability Engineering

by S. C. Malik, Deepak Sinwar, Ashish Kumar, S. R. Gadde, Prasenjit Chatterjee, Bui Thanh Hung
Released March 2023
Publisher(s): Wiley-Scrivener
ISBN: 9781119865018

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Book 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.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Preface
  6. Acknowledgment
  7. 1 Reliability Indices of a Computer System with Priority and Server Failure
    1. 1.1 Introduction
    2. 1.2 Some Fundamentals
    3. 1.3 Notations and Abbreviations
    4. 1.4 Assumptions and State Descriptions
    5. 1.5 Reliability Measures
    6. 1.6 Profit Analysis
    7. 1.7 Particular Case
    8. 1.8 Graphical Presentation of Reliability Indices
    9. 1.9 Real-Life Application
    10. 1.10 Conclusion
    11. References
  8. 2 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm
    1. 2.1 Introduction
    2. 2.2 System Description, Notations, and Assumptions
    3. 2.3 Mathematical Modeling of the System
    4. 2.4 Optimization
    5. 2.5 Results and Discussion
    6. 2.6 Conclusion
    7. References
  9. 3 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design
    1. 3.1 Introduction
    2. 3.2 Problem Formulation of Harmonics
    3. 3.3 Development of Laplacian Artificial Bee Colony Algorithm
    4. 3.4 Discussion
    5. 3.5 Numerical Validation of Proposed Variant
    6. 3.6 Analytical Validation of Proposed Variant
    7. 3.7 Design Analysis of Harmonic Estimator
    8. 3.8 Conclusion
    9. References
  10. 4 Applications of Cuckoo Search Algorithm in Reliability Optimization
    1. 4.1 Introduction
    2. 4.2 Cuckoo Search Algorithm
    3. 4.3 Modified Cuckoo Search Algorithm (MCS)
    4. 4.4 Optimization in Module Design
    5. 4.5 Optimization at Dynamic Implementation
    6. 4.6 Comparative Study of Support of Modified Cuckoo Search Algorithm
    7. 4.7 Results and Discussions
    8. 4.8 Conclusion
    9. References
  11. 5 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula
    1. 5.1 Introduction
    2. 5.2 Assumptions, Notations, and Description of the System
    3. 5.3 Reliability Formulation of Models
    4. 5.4 Some Particular Cases Based on Analytical Analysis of the Model
    5. 5.5 Conclusions Through Result Discussion
    6. References
  12. 6 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization
    1. 6.1 Energy and Environment
    2. 6.2 Sustainable Energy
    3. 6.3 Artificial Intelligence in Industry 4.0
    4. 6.4 Introduction to AI and its Working Mechanism
    5. 6.5 Biodiesel
    6. 6.6 Transesterification Process
    7. 6.7 AI in Biodiesel Applications
    8. 6.8 Conclusion
    9. References
  13. 7 On New Joint Importance Measures for Multistate Reliability Systems
    1. 7.1 Introduction
    2. 7.2 New Joint Importance Measures
    3. 7.3 Discussion
    4. 7.4 Illustrative Example
    5. 7.5 Conclusion
    6. References
  14. 8 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data
    1. 8.1 Introduction
    2. 8.2 Model Description
    3. 8.3 Classical Estimation
    4. 8.4 Bayesian Estimation
    5. 8.5 Simulation Study
    6. 8.6 Real-Life Application
    7. 8.7 Conclusions
    8. References
  15. 9 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data
    1. 9.1 Introduction
    2. 9.2 Fuzzy Lifetime Data
    3. 9.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks
    4. 9.4 Maximum Likelihood Estimation with Exponential Lifetimes
    5. 9.5 Bayes Estimation
    6. 9.6 Numerical Illustration
    7. 9.7 Real Data Study
    8. 9.8 Conclusion
    9. References
  16. 10 Cost-Benefit Analysis of a Redundant System with Refreshment
    1. 10.1 Introduction
    2. 10.2 Notations
    3. 10.3 Average Sojourn Times and Probabilities of Transition States
    4. 10.4 Mean Time to Failure of the System
    5. 10.5 Steady-State Availability
    6. 10.6 The Period in Which the Server is Busy With Inspection
    7. 10.7 Expected Number of Visits for Repair
    8. 10.8 Expected Number of Refreshments
    9. 10.9 Particular Case
    10. 10.10 Cost-Benefit Examination
    11. 10.11 Discussion
    12. 10.12 Conclusion
    13. References
  17. 11 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making
    1. 11.1 Introduction
    2. 11.2 New f-Divergence Measure on Fuzzy Sets
    3. 11.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure
    4. 11.4 Applications for Some Fuzzy f-Divergence Measures
    5. 11.5 Applications in MCDM
    6. 11.6 Conclusion
    7. References
  18. 12 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System
    1. 12.1 Introduction
    2. 12.2 The Assumptions and Notations Used to Solve the System
    3. 12.3 The Probabilities of States Transitions
    4. 12.4 Mean Sojourn Time
    5. 12.5 Mean Time to Failure of the System
    6. 12.6 Steady-State Availability
    7. 12.7 Busy Period of the Server Due to Repair of the Failed Unit
    8. 12.8 Busy Period of the Server Due to Refreshment
    9. 12.9 Estimated Visits Made by the Server
    10. 12.10 Particular Cases
    11. 12.11 Profit Analysis
    12. 12.12 Discussion
    13. 12.13 Conclusion
    14. 12.14 Contribution of Refreshment
    15. 12.15 Future Scope
    16. References
  19. 13 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm
    1. 13.1 Introduction
    2. 13.2 System Description, Notations, and Assumptions
    3. 13.3 Mathematical Modeling of the System
    4. 13.4 Availability Optimization of Proposed Model
    5. 13.5 Results and Discussion
    6. 13.6 Conclusion
    7. References
  20. 14 Investigation of Reliability and Maintainability of Piston Manufacturing Plant
    1. 14.1 Introduction
    2. 14.2 System Description and Data Collection
    3. 14.3 Descriptive Analysis
    4. 14.4 Power Law Process Model
    5. 14.5 Trend and Serial Correlation Analysis
    6. 14.6 Reliability and Maintainability Analysis
    7. 14.7 Conclusion
    8. References
  21. Index
  22. Also of Interest
  23. Wiley End User License Agreement

Product information

  • Title: Computational Intelligence in Sustainable Reliability Engineering
  • Author(s): S. C. Malik, Deepak Sinwar, Ashish Kumar, S. R. Gadde, Prasenjit Chatterjee, Bui Thanh Hung
  • Release date: March 2023
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119865018