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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Smart and Sustainable Intelligent Systems
- Sprache: Englisch
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.
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Seitenzahl: 371
Veröffentlichungsjahr: 2023
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Contents
Cover
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgment
1 Reliability Indices of a Computer System with Priority and Server Failure
1.1 Introduction
1.2 Some Fundamentals
1.3 Notations and Abbreviations
1.4 Assumptions and State Descriptions
1.5 Reliability Measures
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
2.1 Introduction
2.2 System Description, Notations, and Assumptions
2.3 Mathematical Modeling of the System
2.4 Optimization
2.5 Results and Discussion
2.6 Conclusion
References
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
3.4 Discussion
3.5 Numerical Validation of Proposed Variant
3.6 Analytical Validation of Proposed Variant
3.7 Design Analysis of Harmonic Estimator
3.8 Conclusion
References
4 Applications of Cuckoo Search Algorithm in Reliability Optimization
4.1 Introduction
4.2 Cuckoo Search Algorithm
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
5.1 Introduction
5.2 Assumptions, Notations, and Description of the System
5.3 Reliability Formulation of Models
5.4 Some Particular Cases Based on Analytical Analysis of the Model
5.5 Conclusions Through Result Discussion
References
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
7 On New Joint Importance Measures for Multistate Reliability Systems
7.1 Introduction
7.2 New Joint Importance Measures
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
8.1 Introduction
8.2 Model Description
8.3 Classical Estimation
8.4 Bayesian Estimation
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
9.1 Introduction
9.2 Fuzzy Lifetime Data
9.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks
9.4 Maximum Likelihood Estimation with Exponential Lifetimes
9.5 Bayes Estimation
9.6 Numerical Illustration
9.7 Real Data Study
9.8 Conclusion
References
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
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
11.6 Conclusion
References
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
13 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm
13.1 Introduction
13.2 System Description, Notations, and Assumptions
13.3 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
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
Also of Interest
Wiley End User License Agreement
List of Figures
Chapter 1
Figure 1.1 State transition diagram.
Figure 1.2 MTSF vs hardware failure rate (
x
1
).
Figure 1.3 Availability vs hardware failure rate (x
1
).
Figure 1.4 Profit (P) vs hardware failure rate (x
1
).
Chapter 2
Figure 2.1 Configuration diagram of turbine.
Figure 2.2 State changeover diagram of a turbine subsystem in steam turbine power plant.
Figure 2.3 Effect of variation in failure rates on system’s availability w.r.t. failure rate α
1
.
Figure 2.4 Effect of variation in repair rates on system’s availability w.r.t. repair rate β
1
.
Chapter 3
Figure 3.1 Flowchart of proposed LABC algorithm based harmonic estimator design.
Figure 3.2 Proposed Laplacian factor.
Figure 3.3 Shape curves of benchmark functions black.
Figure 3.4 Convergence analysis of unimodal functions.
Figure 3.5 Convergence analysis of multimodal functions.
Figure 3.6 Convergence analysis of fixed-dimension functions.
Figure 3.7 Box plot analysis of proposed algorithm.
Figure 3.8 Estimated waves of problem 1 and problem 2 under no noise.
Figure 3.9 Estimated waves of problem 1 under noise.
Figure 3.10 Trajectory analysis of problem 1 phase and amplitude.
Figure 3.11 Estimated waves of problem-2 under noise.
Figure 3.12 Trajectory analysis of problem-2 phase and amplitude.
Chapter 4
Figure 4.1 Optimization process.
Figure 4.2 Graphical representation of fitness values for MCS and PSO.
Chapter 5
Figure 5.1 Reliability block diagram of system.
Figure 5.2 State transition diagram.
Figure 5.3 Availability against time.
Figure 5.4 Reliability against time.
Figure 5.5 Mean time to failure against failure rate.
Figure 5.6 Cost-benefit against time.
Chapter 6
Figure 6.1 Stages of Industrial Revolution from industry 1.0 to 4.0.
Figure 6.2 Stricture of a biological neuron.
Figure 6.3 Stricture of artificial neuron.
Figure 6.4 Different feedstocks for biodiesel production.
Figure 6.5 Transesterification reaction process.
Figure 6.6 Biodiesel production stages.
Chapter 7
Figure 7.1 Series system.
Figure 7.2 Multistate joint importance measures (1, 2, 3).
Figure 7.3 Multistate joint importance measures (1, 2, 3).
Chapter 9
Figure 9.1 Fuzzy membership function.
Chapter 10
Figure 10.1 State transition diagram.
Chapter 12
Figure 12.1 State transition diagram.
Figure 12.2 MTSF vs. Refreshment rate (θ) →.
Figure 12.3 Availability vs. Refreshment rate (θ) →.
Figure 12.4 Profit vs. Refreshment rate (θ) →.
Figure 12.5 Availability vs. Repair rate (θ) →.
Figure 12.6 Profit vs. Repair rate (θ) →.
Chapter 13
Figure 13.1 Configuration diagram of HRSG.
Figure 13.2 State changeover diagram of HRSG in steam turbine power plant.
Figure 13.3 Effect of variation in failure rates on system’s availability w.r.t. failure rate
α
3
.
Figure 13.4 Effect of variation in repair rates on system’s availability w.r.t. repair rate
β
3
.
Chapter 14
Figure 14.1 System description.
Figure 14.2 Histogram of TTR of plant.
Figure 14.3 Histogram of TBF of plant.
Figure 14.4 Pareto chart of plant.
Figure 14.5 Box plot of TTR of plant.
Figure 14.6 Box plot of TBF of plant.
Figure 14.7 Graphical test for serial correlation.
Figure 14.8 Probability curve of plant’s TTR.
Figure 14.9 (a) Hazard rate plot of TTR. (b) Survival rate plot of TTR.
Figure 14.10 Scatter plot of TBF vs. TTR
Guide
Cover
Series Page
Title Page
Copyright Page
Dedication Page
Table of Contents
Preface
Acknowledgment
Begin Reading
Index
Also of Interest
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Scrivener Publishing
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Sustainable Computing and Optimization
Series Editor: Prasenjit Chatterjee, Morteza Yazdani and Dilbagh Panchal
Scope: The objective of “Sustainable Computing and Optimization” series is to bring together the global research scholars, experts, and scientists in the research areas of sustainable computing and optimization from all over the world to share their knowledge and experiences on current research achievements in these fields. The series aims to provide a golden opportunity for global research community to share their novel research results, findings, and innovations to a wide range of readers, present globally. Data is everywhere and continuing to grow massively, which has created a huge demand for qualified experts who can uncover valuable insights from data. The series will promote sustainable computing and optimization methodologies in order to solve real life problems mainly from engineering and management systems domains. The series will mainly focus on the real life problems, which can suitably be handled through these paradigms.
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Computational Intelligence in Sustainable Reliability Engineering
Edited by
S. C. Malik
Department of Statistics, M.D. University, Rohtak, India
Deepak Sinwar
Department of Computer and Communication Engineering, Manipal University, Jaipur, India
Ashish Kumar
Department of Mathematics and Statistics, Manipal University, Jaipur, India
S. R. Gadde
Department of Statistics, The University of Dodoma, Tanzania
Prasenjit Chatterjee
Department of Mechanical Engineering, MCKV Institute of Engineering, West Bengal, India
and
Bui Thanh Hung
Faculty of Information Technology, Artificial Intelligence Laboratory, Ton Duc Thang University, Ho Chi Minh City, Vietnam
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86501-8
Cover image: Pixabay.ComCover design by Russell Richardson
The editors would like to dedicate this book to their parents, family members, friends and readers.
Preface
With the design of every new product, the world is witnessing the continuous development brought on by cross-disciplinary technologies. Instead of taking raw materials and sending them through a real manufacturing process that repeatedly combats tolerances, errors, and energy consumption to arrive at the final product, the assembly details can be directly input into the computation model in order to obtain the material characteristics as output to reduce effort and process costs. To ensure maximum reliability of product development, it is desired that the manufacturing process be driven by optimization. However, even though optimization has previously been applied for various fields, over the past two decades, computational optimization has become very popular for industrial optimizations. Computational intelligence-based optimization is one of several computational techniques that help achieve sustainability in product design and development phases. Among computational intelligence-based techniques, metaheuristic optimization is found to be specifically suitable for industrial optimizations. There are mainly two types of metaheuristic approaches; single-solution based and population based. As per the applications in the field of industrial optimization, this book mainly focuses on population-based (swarm intelligence) metaheuristic approaches.
Swarm intelligence is an important sub-area of optimization that helps develop sustainable materials at nano-, micro-, meso- and macro-levels by identifying the optimum values for different parameters. With the exponential rise in demand for sustainable materials for various purposes, optimization has played an important role over the last few years. Not only is materials data available for researchers and scientists, but sufficient processing resources are also available, which need to be optimized through AI techniques.
Traditional techniques employed by researchers are often cumbersome, expensive and lack sustainability. Hence, there is always a need for having recourse to time-efficient, fail-safe, cheaper intelligent technologies to address problems and ensure long-term sustainability. Since the existing literature available in this respect is nonexistent, this book is proposed to serve as a treatise and knowledge base for the community to inspire them to adapt environment-friendly and sustainable solutions for the future.
This book focuses on developing advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing with the objective of ensuring sustainability. It reveals applications of different models of evolutionary algorithms in the field of optimization with the objective of solving 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 to reliability and maintainability theory. A chapter-wise summary of the information presented herein follows.
Chapter 1 presents a stochastic model for reliability indices of a computer system with priority and server failure. The model is analyzed by using the semi-Markov process and regenerative point technique. The reliability indices, such as mean time to system failure (MTCSF), availability, busy period of the server due to hardware repair and software upgradation, expected number of treatments given to the server, expected number of hardware repair, and software upgradation, are obtained for arbitrary values of the parameters. The profit analysis of the system model has also been carried out to discern the usefulness of the system under different parametric situations.
Chapter 2 presents a study that optimizes the availability of a turbine unit (TU) of a steam turbine power plant (STPP) using mathematical modeling and a genetic algorithm. The mathematical model is developed using the Markovian birth-death process (MBDP) and Chapman-Kolmogorov differential equations derived for the proposed model. The analytical solution of the mathematical model is derived for a particular case by considering exponential distribution for random variables associated with failure and repair rates. By using a nature-inspired algorithm (NIA), namely a genetic algorithm (GA), an effort is made to attain the global solution of the TU.
Chapter 3 covers the development of the Laplacian artificial bee colony (LABC) algorithm for effective harmonic estimator design. For designing the estimator, a hybrid approach based on least square error minimization with the help of a new version of the artificial bee colony algorithm is proposed. The proposed version employs a Laplacian factor-based update equation in the scout bee phase. For proving the modification meaningful, first the proposed algorithm is tested on several standard benchmark problems, and then it is applied to the estimator design problem. Results reported in on both parts indicate that the proposed modification is meaningful and the performance of the LABC algorithm is comparable with that of many other state-of-the-art algorithms.
Chapter 4 discusses the applications of the cuckoo search algorithm in reliability optimization, which is a novel nature-inspired algorithm that is used to solve complex optimization problems. The algorithm depends on the brood-parasitic strategy of cuckoo species. The usage of Lévy flights is used to produce new candidate resolutions. It can improve the relationship between exploration and exploitation towards the potential of searching. It can also be used in solving engineering problems such as embedded systems, distribution of networks, and scheduling problems. In this chapter, a study of the reliability of the software at static and runtime is performed and the results are also discussed.
Chapter 5 carries out a performance evaluation of the series-parallel computer system with a Gumbel-Hougaard copula family. To analyze the reliability of the system, the partial differential equations are derived from the system’s schematic diagram in which reliability measures of system strength, such as reliability, availability, mean time to failure (MTTF), and cost function, are computed. The MTTF of devices, such as workstation, hub, and router, obeys exponential distribution whereas the corresponding repair time follows two different distributions, namely general and copula distribution. The findings of the study are depicted with the help of suitable diagrams and tabular representations.
Chapter 6 covers the applications of artificial intelligence (AI) in sustainable energy development and utilization. To combat the energy and environmental crises, clean and renewable fuels like biofuels are popular as petrodiesel replacement fuels. Biofuels can be obtained from different feedstocks and are successfully tested in diesel engines. However, several parameters influence the output results during their production and engine testing. The accurate prediction of end results is considered challenging with the traditional techniques. Therefore, AI techniques have emerged as being the most successful in solving nonlinear problems and achieving a high success rate in prediction. In this chapter, different AI techniques that have been successfully used in finding a feasible solution for complex problems in biodiesel production and engine testing are discussed in detail.
Chapter 7 introduces a new joint reliability achievement worth (JRAW), joint reliability reduction worth (JRRW), and joint reliability Fussell-Vesely (JRFV) measure for three multistate components of a multistate system. This is a new approach to detect the joint effect of a group of components in improving system reliability. The differencing technique is used in the proposed measures. A steady-state performance level distribution restricted to the component’s states is used to evaluate the proposed measures. The universal generating function (UGF) technique is applied for the evaluation of proposed joint importance measures with suitable examples. Chapter 8 presents some inferences about inverse Rayleigh distribution based on joint progressive Type-II censoring. The maximum likelihood estimation and the corresponding asymptotic confidence interval estimation are used as the classical estimation methods. The Bayes estimates are calculated under the squared error loss function (SELF) using Tierney-Kadane’s approximation and Metropolis-Hastings algorithm, along with the construction of Bayes estimates highest posterior density credible intervals. A Markov chain Monte Carlo simulation study is carried out to compare different estimation methods and a real-life problem is discussed for illustrative purposes.
Chapter 9 deals with component reliability estimation through competing risk analysis of fuzzy lifetime data. In many cases, the lifetimes of systems are not precisely observed, or they are reported in “vague” terms. This imprecision or vagueness in data can be dealt with more accurately by incorporating fuzzy concepts. In this chapter, a competing risk analysis of lifetime data is performed by considering lifetimes as fuzzy numbers. Using different membership functions, the authors provide procedures for maximum likelihood and a Bayesian estimation of component reliability. They also evaluate bootstrap confidence intervals and the highest posterior density intervals. To observe the impact of various membership functions on the considered estimators, a comprehensive simulation study has been carried out. Finally, a real data set of small electric appliances has been analyzed.
Chapter 10 discusses the cost-benefit analysis of a redundant system with the provision of refreshment. Sometimes, due to some system-oriented snags and glitches, system performance may be hindered that can be overcome by repair. The goal of this chapter is to look at the survey of cost-benefit of a two-unit system with a single unit that can operate the system and another unit held as a spare in case of server failure, with refreshment provided to the server on demand.
Chapter 11 introduces a few novel inequalities of fuzzy measures and establishes the bounds in terms of triangular discrimination. Some new relations between new and existing fuzzy divergence measures are obtained with the help of the properties of a convex function and a new f-divergence measure. The utility of new fuzzy divergence measures in multi-criteria decision-making problems is also presented for better understanding.
Chapter 12 discusses the contribution of refreshment provided to the server during the job of repairing a cold standby system. The concept of probabilities of state transitions is presented followed by mean sojourn time and mean time to failure of the system. When calculating steady-state availability, a busy period of the server due to repair of the failed unit and a busy period of the server due to refreshment is computed followed by estimated visits made by the server. Novel conclusions are drawn based on considering particular cases and profit analysis.
Chapter 13 deals with stochastic modeling and availability optimization of a heat recovery steam generator using a genetic algorithm. The study presented in this chapter proposes a novel mathematical model for a heat recovery steam generator (HRSG) system to assess its availability. For this purpose, a state transition diagram is developed using the Markov birth-death process by considering all time-dependent failure and repair rates as exponentially distributed. The Chapman-Kolmogorov differential-difference equations are derived for the proposed model. The availability of the proposed model is optimized using a genetic algorithm to attain the global solution.
Chapter 14 investigates the reliability and maintainability of a piston manufacturing plant. For this analysis, data on time to repair and the number of failures was collected over two years. A descriptive analysis of the subsystems was performed along with trend and serial correlation testing. The best-fitted repair and failure time distributions among Weibull, normal exponential and lognormal distributions were investigated. The useful parameters corresponding to best-fitted distribution were estimated using U-statistics methodology and non-homogeneous Poisson process–power law process (NHPP-PLP). The reliability, availability, and hazard rates of the entire plant were calculated. The results were stored in numerical and graphical order concerning time to highlight the importance of the study. The model is useful not only in assessing the anticipated time for planning a maintenance schedule of a plant but also in terms of identifying the occurrence of failures in manufacturing plants.
EditorsProf. S. C. Malik
Department of Statistics
M.D. University, Rohtak, India
Dr. Deepak Sinwar
Department of Computer and Communication Engineering
Manipal University Jaipur, India
Dr. Ashish Kumar Department of Mathematics and StatisticsManipal University Jaipur, IndiaProf. S. R. GaddeDepartment of StatisticsThe University of Dodoma, TanzaniaDr. Prasenjit ChatterjeeDepartment of Mechanical Engineering MCKV Institute of Engineering, West Bengal, IndiaDr. Bui Thanh HungFaculty of Information Technology, Artificial Intelligence LaboratoryTon Duc Thang University, Ho Chi Minh City, VietnamDecember 2022
Acknowledgment
The editors wish to express their sincere thanks and appreciation to those who provided valuable support, constructive suggestions and assisted in editing this book.
This book would not have been possible without the valuable scholarly contributions of the authors.
The editors avow the endless support and motivation from their family members and friends.
Mere words cannot express the editors’ deep gratitude to the entire Scrivener Publishing team, particularly Mr. Martin Scrivener for keeping faith and showing the right path to accomplish this very timely book.
Finally, the editors take this opportunity to thank all the readers and expect that this book will continue to inspire and guide them in high end researches.
The Editors
