Reliability Engineering for Industrial Processes
Description
This book explores how transformative changes driven by the new-age economy can bring about improvements in a company's engineering and manufacturing capabilities.
The new-age economy is driven by advanced engineering and manufacturing practices, processes, and technologies, including the Internet of Things (IoT), Cloud Computing, Blockchain, Artificial Intelligence, Robotics, Cyber-Physical Systems (CPS), and Internet-enabled systems to automate industrial processes.
Today's business dynamics are governed by uncertainties, disruptions, complexities, and ambiguities that demand quicker and more intelligent decisions. These changes could relate a renaissance in the company's engineering and manufacturing capabilities. To sustain these volatile and ever-changing business dynamics, Industry 4.0 and 5.0 have revolutionized how organizations operate and make intelligent business decisions. Moreover, the extensive role of business analytics has overcome the limitations ofclassical computing through new technologies and intelligent computing methodologies.
Over the past few years, much emphasis has been given to investing in developing hardware and programming frameworks for achieving computational intelligence using fuzzy logic, evolutionary computation, neural networks, probabilistic methods, and learning theory. Within this frame of reference, the reliability, quality, and maintenance of complex industrial and manufacturing systems are essential for organizations to utilize them successfully for informed decisions.
This book focuses on studies that provide new solutions for system reliability, quality, security, and maintainability using quantitative and qualitative research. It emphasizes developments and problems in systems engineering management, systems integration, software and hardware engineering, and the development process.More details
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Persons
Prof. (Dr.) P. K. Kapur is Director of Amity Center for Interdisciplinary Research, Amity University, Noida. He is Former Dean of the Faculty of Mathematical Sciences and Former Head of the Department of Operational Research, University of Delhi. His vast research experience in the areas of Software Reliability, Optimization, Innovation Diffusion Modeling in Marketing, and Multi-Criteria Decision Making (MCDM), Big Data Projects Adoption, and other areas of management, is illustrated through his work with nearly 400 research paper publications in top international and national journals/proceedings of repute, and supervision of over 44 Ph.D. and 25 M.Phil. scholars. Presently, he is also Member of the University Research Council, Amity University, Noida.
He has authored and edited more than ten books/conference volumes, many of which have been published by Springer. He is also Editor-in-Chief of the International Journal of Systems Assurance Engineering and Management (IJSAEM) published by Springer. He is Founder President of the Society for Reliability Engineering, Quality and Operations Management (SREQOM) since 2000 and Former President of the Operational Research Society of India (ORSI). He has executed various research projects from UGC, DRDO in the areas of Software Reliability and Innovation Diffusion Modeling; has delivered keynote addresses at various prestigious international conferences; and has been invited to deliver lectures in various universities in Sweden, Denmark, South Africa, Russia, Serbia, Nepal, Dubai, to name some.
Prof. (Dr.) Hoang Pham is Distinguished Professor and Former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University, New Jersey. Before joining Rutgers, he was Senior Engineering Specialist with the Boeing Company and the Idaho National Engineering Laboratory. He has been served as Editor-in-Chief, Editor, Associate Editor, Guest Editor, and Board Member of many journals. He is Editor of Springer Book Series in Reliability Engineering and has served as Conference Chair and Program Chair of over 40 international conferences. He is Author or Co-author of six books and has published over 200 journal articles, 100 conference papers, and edited 17 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 40 invited keynote and plenary speeches at many international conferences and institutions. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Industrial Engineers (IIE).
Prof. (Dr.) Gurinder Singh is a group vice chancellor, Amity Universities, Director General, Amity Group of Institutions, India, and a vice chairman, Global Foundation for Learning Excellence, and has an experience of more than 27 years in institutional building, teaching, research, and industry. He holds a doctorate and a postgraduate degree from Jamia Millia & Indian Institute of Foreign Trade, Delhi. He is a well-acknowledged author and a researcher who has published more than 100 research papers, 15 books and is an editor of ten prestigious research journals. He has given key note lectures at various forums which includes speaking at Harvard Business School, NYU, University of Leeds, University of Berkeley, National University of Singapore, NTU, MIT, and many more.
Dr. Vivek Kumar is an assistant professor at Amity International Business School, Amity University, Noida, India. He has received his Ph.D., M.Phil., and M.Sc. in Operational Research from Department of Operational Research, University of Delhi, Delhi, India. He has published papers in International Journals and Conference Proceedings of repute. He is a lifetime member of the Society for Reliability Engineering, Quality, and Operations Management (SREQOM). He is an associateeditor of International Journal of Systems Assurance Engineering and Management (IJSAEM), Springer.
Content
Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning.- Reliability and sensitivity analysis of a wastewater treatment plant operating with two blowers as a single system.- A Review on Cardiovascular Disease / Heart Disease By Machine Learning Prediction.- A Role of Network Data Envelopment Analysis Approach in Manufacturing Industry: Review of last 5 years.- Exploring Software Systems Engineering through complexity of code changes: A Study based on bugs count, features improvement and new add-ons.- Generating image captions in Hindi based on Encoder-Decoder based Deep Learning techniques.- Fault Removal Efficiency: A Key Driver in Software Reliability Growth Modelling.- Analysis of Progressively Censored Repair Time of Airborne Communication Transceiver with Burr-Hatke Exponential Model.- Bug Prediction Techniques: Analysis and Review.- A Review on Kidney Failure Prediction Using Machine Learning Models.- Machine Learning Based Remaining Useful Life Estimation - Concept and Case Study.- Modelling Software Reliability Growth incorporating Testing Coverage Function and Fault Reduction Factor.- Software Defect Prediction using Abstract Syntax Trees Features and Object - Oriented metrics.- A Review of Alzheimer's Disease Identification by Machine Learning.- Weighted Entropic and Divergence Models in Probability Spaces and their Solicitations for Influencing an Imprecise Distribution.- Considering multiplicative noise in a software reliability growth model using stochastic differential equation.- An Insight into Code Smell Detection Tool.- Efficiency of Divergence Measure in Fuzzy TOPSIS Algorithm for Multi-Attribute Decision Making.- Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study.- Reliability Perspective of Software and Hardware Models.- Stress-Strength Modelling for a New Modified Lindley Distribution Under Progressively Censored Data.- Imperfect debugging, testing coverage, and compiler error-based SRGM with two types of faults under the uncertainty of the operating environment.- A statistical approach to estimate severe accident vehicle collision probability inside a multi-lane road tunnel with unidirectional traffic flow.
