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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.
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. He has supervised 35 research scholars for the PhD degree and 23 scholars for the M. Phil degree. Prof. Malik has 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 different 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 on intelligent decision-making, supply chain management, optimization techniques, risk and sustainability modelling. Dr. Chatterjee is the Editor-in-Chief of Journal of Decision Analytics and Intelligent Computing. He 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).
Bui Thanh Hung, PhD, is the Director of Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.
Preface xv
Acknowledgment xxi
1 Reliability Indices of a Computer System with Priority and Server Failure 1
S.C. Malik, R.K. Yadav and N. Nandal
1.1 Introduction 2
1.2 Some Fundamentals 4
1.2.1 Reliability 4
1.2.2 Mean Time to System Failure (MTSF) 4
1.2.3 Steady State Availability 4
1.2.4 Redundancy 5
1.2.5 Semi-Markov Process 5
1.2.6 Regenerative Point Process 6
1.3 Notations and Abbreviations 6
1.4 Assumptions and State Descriptions 8
1.5 Reliability Measures 9
1.5.1 Transition Probabilities 9
1.5.2 Mst 10
1.5.3 Reliability and MTCSF 10
1.5.4 Availability 11
1.5.5 Expected Number of Hardware Repairs 12
1.5.6 Expected Number of Software Upgradations 13
1.5.7 Expected Number of Treatments Given to the Server 14
1.5.8 Busy Period of Server Due to H/w Repair 15
1.5.9 Busy Period of Server Due to Software Upgradation 16
1.6 Profit Analysis 17
1.7 Particular Case 18
1.8 Graphical Presentation of Reliability Indices 19
1.9 Real-Life Application 20
1.10 Conclusion 21
References 21
2 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 23
Monika Saini, Nivedita Gupta and Ashish Kumar
2.1 Introduction 23
2.2 System Description, Notations, and Assumptions 25
2.2.1 System Description 25
2.2.2 Notations 27
2.2.3 Assumptions 28
2.3 Mathematical Modeling of the System 28
2.4 Optimization 33
2.4.1 Genetic Algorithm 33
2.5 Results and Discussion 34
2.6 Conclusion 36
References 45
3 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 47
Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar
3.1 Introduction 48
3.2 Problem Formulation of Harmonics 52
3.3 Development of Laplacian Artificial Bee Colony Algorithm 54
3.3.1 Basic Concepts of ABC 54
3.3.2 The Proposed LABC Algorithm 56
3.4 Discussion 58
3.5 Numerical Validation of Proposed Variant 58
3.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 59
3.5.2 Benchmark Test on CEC-17 Functions 70
3.6 Analytical Validation of Proposed Variant 72
3.6.1 Convergence Rate Test 75
3.6.2 Box Plot Analysis 77
3.6.3 Wilcoxon Rank Sum Test 77
3.6.4 Scalability Test 81
3.7 Design Analysis of Harmonic Estimator 81
3.7.1 Assessment of Harmonic Estimator Design Problem 1 81
3.7.2 Assessment of Harmonic Estimator Design Problem 2 87
3.8 Conclusion 92
References 93
4 Applications of Cuckoo Search Algorithm in Reliability Optimization 97
V. Kaviyarasu and V. Suganthi
4.1 Introduction 98
4.2 Cuckoo Search Algorithm 98
4.2.1 Performance of Cuckoo Search Algorithm 98
4.2.2 Levy Flights 99
4.2.3 Software Reliability 99
4.3 Modified Cuckoo Search Algorithm (MCS) 100
4.4 Optimization in Module Design 102
4.5 Optimization at Dynamic Implementation 103
4.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 104
4.7 Results and Discussions 105
4.8 Conclusion 107
References 108
5 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 109
Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao
5.1 Introduction 110
5.2 Assumptions, Notations, and Description of the System 112
5.2.1 Notations 112
5.2.2 Assumptions 114
5.2.3 Description of the System 114
5.3 Reliability Formulation of Models 116
5.3.1 Solution of the Model 117
5.4 Some Particular Cases Based on Analytical Analysis of the Model 120
5.4.1 Availability Analysis 120
5.4.2 Reliability Analysis 121
5.4.3 Mean Time to Failure (MTTF) 122
5.4.4 Cost-Benefit Analysis 124
5.5 Conclusions Through Result Discussion 125
References 126
6 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 129
Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman
6.1 Energy and Environment 130
6.2 Sustainable Energy 130
6.3 Artificial Intelligence in Industry 4.0 131
6.4 Introduction to AI and its Working Mechanism 132
6.5 Biodiesel 135
6.6 Transesterification Process 136
6.7 AI in Biodiesel Applications 138
6.8 Conclusion 140
References 140
7 On New Joint Importance Measures for Multistate Reliability Systems 145
Chacko V. M.
7.1 Introduction 145
7.2 New Joint Importance Measures 147
7.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 148
7.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 150
7.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 152
7.3 Discussion 153
7.4 Illustrative Example 154
7.5 Conclusion 157
References 157
8 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 159
Kapil Kumar and Anita Kumari
8.1 Introduction 159
8.2 Model Description 161
8.3 Classical Estimation 163
8.3.1 Maximum Likelihood Estimation 163
8.3.2 Asymptotic Confidence Interval 164
8.4 Bayesian Estimation 166
8.4.1 Tierney-Kadane’s Approximation 167
8.4.2 Metropolis-Hastings Algorithm 169
8.4.3 HPD Credible Interval 170
8.5 Simulation Study 170
8.6 Real-Life Application 176
8.7 Conclusions 177
References 177
9 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 181
Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer
9.1 Introduction 182
9.2 Fuzzy Lifetime Data 183
9.2.1 Fuzzy Set 183
9.2.2 Fuzzy Numbers and Membership Function 184
9.2.3 Fuzzy Event and its Probability 187
9.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 187
9.4 Maximum Likelihood Estimation with Exponential Lifetimes 189
9.4.1 Bootstrap Confidence Interval 192
9.5 Bayes Estimation 192
9.5.1 Highest Posterior Density Confidence Estimates 194
9.6 Numerical Illustration 195
9.6.1 Simulation Study 196
9.6.2 Reliability Analysis Using Simulated Data 210
9.7 Real Data Study 212
9.8 Conclusion 212
References 215
10 Cost-Benefit Analysis of a Redundant System with Refreshment 217
M.S. Barak and Dhiraj Yadav
10.1 Introduction 218
10.2 Notations 219
10.3 Average Sojourn Times and Probabilities of Transition States 220
10.4 Mean Time to Failure of the System 223
10.5 Steady-State Availability 223
10.6 The Period in Which the Server is Busy With Inspection 224
10.7 Expected Number of Visits for Repair 227
10.8 Expected Number of Refreshments 227
10.9 Particular Case 228
10. 10 Cost-Benefit Examination 230
10.11 Discussion 230
10.12 Conclusion 233
References 233
11 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 235
Ram Naresh Saraswat and Sapna Gahlot
11.1 Introduction 235
11.2 New f-Divergence Measure on Fuzzy Sets 237
11.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 239
11.4 Applications for Some Fuzzy f-Divergence Measures 241
11.5 Applications in MCDM 244
11.5.1 Case Study 246
11.6 Conclusion 247
References 248
12 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 251
M.S. Barak, Ajay Kumar and Reena Garg
12.1 Introduction 252
12.2 The Assumptions and Notations Used to Solve the System 254
12.3 The Probabilities of States Transitions 256
12.4 Mean Sojourn Time 257
12.5 Mean Time to Failure of the System 257
12.6 Steady-State Availability 258
12.7 Busy Period of the Server Due to Repair of the Failed Unit 259
12.8 Busy Period of the Server Due to Refreshment 259
12.9 Estimated Visits Made by the Server 260
12.10 Particular Cases 261
12.11 Profit Analysis 262
12.12 Discussion 262
12.13 Conclusion 264
12.14 Contribution of Refreshment 265
12.15 Future Scope 265
References 265
13 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 269
Monika Saini, Nivedita Gupta and Ashish Kumar
13.1 Introduction 270
13.2 System Description, Notations, and Assumptions 271
13.2.1 System Description 271
13.2.2 Notations 272
13.2.3 Assumptions 273
13.3 Mathematical Modeling of the System 273
13.4 Availability Optimization of Proposed Model 278
13.5 Results and Discussion 280
13.6 Conclusion 285
References 285
14 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 287
Monika Saini, Deepak Sinwar and Ashish Kumar
14.1 Introduction 288
14.2 System Description and Data Collection 290
14.3 Descriptive Analysis 294
14.4 Power Law Process Model 295
14.5 Trend and Serial Correlation Analysis 300
14.6 Reliability and Maintainability Analysis 302
14.7 Conclusion 306
References 307
Index 311
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