This book is essential for any leader seeking to understand how to leverage intelligent automation and predictive maintenance to drive innovation, enhance productivity, and minimize downtime in their manufacturing processes.
Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. This book emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms.
Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial Intelligence and Machine Learning for Industry 4.0offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.
M. Thirunavukkarasan, PhD is an assistant professor in the School of Computer Science and Engineering at the Vellore Institute of Technology with over 15 years of research and teaching experience. He has published papers in several international conferences and journals and given keynote speeches at many international conferences. His research interests include Internet of Things (IoT), wireless sensor networks, wireless communication, cloud computing, artificial intelligence, and machine learning.
S.A. Sahaaya Arul Mary, PhD is a professor in the School of Computer Science and Engineering, Vellore Institute of Technology with over 29 years of teaching and over 15 years of research experience. She has over 70 publications in various reputed journals and conferences and reviewed over 35 papers in addition to mentoring aspiring PhD students. Her research includes software engineering, data mining, machine learning, and artificial intelligence.
Sathiyaraj R., PhD is an assistant professor in the Department of Computer Science and Engineering at Gandhi Institute of Technology and Management University in Bangalore, India. He has contributed to two books, served as lead editor for an additional two books, and published five patents and over 20 articles in various international journals and conferences. His research interests include machine learning, big data analytics, and intelligent systems.
G.S. Pradeep Ghantasala, PhD is a professor in the Department of Computer Science and Engineering, at Alliance University with over 16 years of academic experience. He has contributed to internationally published books, chapters, patents, and numerous papers in journals and conferences. He also serves as an editor and reviewer for several journals. His research interests include machine learning, deep learning, healthcare applications, and software engineering applications.
Mudassir Khan, PhD is an assistant professor in the Department of Computer Science at King Khalid University with over ten years of teaching experience. He has published over 25 papers in international journals and conferences and one patent. He is a member of various technical and professional societies including the Institute for Electrical and Electronics Engineers and Computer Science Teachers Association. His research interests include big data, deep learning, machine learning, eLearning, fuzzy logic, image processing, and cyber security.
