IEMERA is a three-day International Conference specially designed with cluster of scientific and technological sessions, providing a common platform for the researchers, academicians, industry delegates across the globe to share and exchange their knowledge and contribution. The emerging areas of research and development in Electrical, Electronics, Mechanical and Software technologies are major focus areas. The conference is equipped with well-organized scientific sessions, keynote and plenary lectures, research paper and poster presentations and world-class exhibitions. Moreover, IEMERA 2020 facilitates better understanding of the technological developments and scientific advancements across the world by showcasing the pace of science, technology and business areas in the field of Energy Management, Electronics, Electric & Thermal Power, Robotics and Automation.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence. The intense development of information technology allows for designing more complex systems as well as creating more detailed models of real-world systems which forces traditional reliability engineering approaches based on Boolean algebra, probability theory, and statistics to embrace the world of data science. The works deal with methodological developments as well as applications in the development of safe and reliable systems in various kinds of distribution networks, in the development of highly reliable healthcare systems, in finding weaknesses in systems with the human factor, or in reliability analysis of large information systems and other software solutions. In this book, experts from various fields of reliability engineering and computational intelligence present their view on the risks, the opportunities and the synergy between reliability engineering and computational intelligence that have been developed separately but in recent years have found a way to each other. The topics addressed include the latest advances in computing technology to improve the real lives of millions of people by increasing safety and reliability of various types of real-life systems by increasing the availability of software services, reducing the accident rate of means of transport, developing high reliable patient-specific health care, or generally, save cost and increase efficiency in the work and living environment. Though this book, the reader has access to professionals and researchers in the fields of reliability engineering and computational intelligence that share their experience in merging the two as well as an insight into the latest methods, concerns and application domains.

This volume includes chapters presenting applications of different metaheuristics in reliability engineering, including ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization. It also presents chapters devoted to cellular automata and support vector machines, and applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe aspects of imprecise reliability and applications of fuzzy and vague set theory.

Computational intelligence is rapidly becoming an essential part of reliability engineering. This book offers a wide spectrum of viewpoints on the merger of technologies. Leading scientists share their insights and progress on reliability engineering techniques, suitable mathematical methods, and practical applications. Thought-provoking ideas are embedded in a solid scientific basis that contribute to the development the emerging field. This book is for anyone working on the most fundamental paradigm-shift in resilience engineering in decades. Scientists benefit from this book by gaining insight in the latest in the merger of reliability engineering and computational intelligence. Businesses and (IT) suppliers can find inspiration for the future, and reliability engineers can use the book to move closer to the cutting edge of technology.

How to minimize the global problem of e-waste KEY FEATURES ● Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB. ● Cutting edge coverage on building intelligent systems for reliability analysis. ● Includes numerous techniques and methods to identify failure and reliability parameters. DESCRIPTION Intelligent Reliability Analysis using MATLAB and AI explains a roadmap to analyze and predict various electronic components’ future life and performance reliability. Deeply narrated and authored by reliability experts, this book empowers the reader to deepen their understanding of reliability identification, its significance, preventive measures, and various techniques. The book teaches how to predict the residual lifetime of active and passive components using an interesting use case on electronic waste. The book will demonstrate how the capacity of re-usability of electronic components can benefit the consumer to reuse the same component, with the confidence of successful operations. It lists key attributes and ways to design experiments using Taguchi’s approach, based on various acceleration factors. This book makes it easier for readers to understand reliability modeling of active and passive components using the Artificial Neural Network, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS). The book keeps you engaged with a systematic and detailed explanation of step-wise MATLAB-based implementation of electronic components. These explanations and illustrations will help the readers to predict fault and failure well before time. WHAT YOU WILL LEARN ● Optimize various acceleration factors for exploring the residual life of components experimentally. ● Design an intelligent model to predict the upcoming faults and failures of electronic components and make provision for timely replacement of the fault components. ● Design experiments using Taguchi’s approach. ● Understand reliability modeling of active and passive components using the Artificial Neural Network and Fuzzy Logic. WHO THIS BOOK IS FOR This book is for current and aspiring emerging tech professionals, researchers, students, and anyone who wishes to understand and diagnose the product life of electronic components using the power of artificial intelligence and various experimental techniques. TABLE OF CONTENTS 1. RELIABILITY FUNDAMENTALS 2. RELIABILITY MEASURES 3. REMAINING USEFUL LIFETIME ESTIMATION TECHNIQUES 4. INTELLIGENT MODELS FOR RELIABILITY PREDICTION 5. ACCELERATED LIFE TESTING 6. EXPERIMENTAL TESTING OF ACTIVE AND PASSIVE COMPONENTS 7. INTELLIGENT MODELING FOR RELIABILITY ASSESSMENT USING MATLAB

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.