Computational intelligence in sustainable reliability engineering / edited by S.C. Malik [and more]

Includes index.

Table of Contents
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

About the Author
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).

Bui Thanh Hung, PhD, is the Director of the 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.