AI for Predictive Maintenance in Manufacturing
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A01=SreeRam Nimmagadda
Author_SreeRam Nimmagadda
Category=UYQ
Engineering and Technology
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eq_computing
eq_isMigrated=1
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eq_nobargain
eq_non-fiction
Product details
- ISBN 9781779569813
- Dimensions: 152 x 229mm
- Publication Date: 01 Mar 2026
- Publisher: Arcler Press
- Publication City/Country: CA
- Product Form: Hardback
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Artificial intelligence is reshaping the manufacturing sector by enabling predictive maintenance—anticipating equipment failures before they occur. Through data-driven insights, industries can minimize downtime and enhance operational efficiency. AI for Predictive Maintenance in Manufacturing explores the intersection of AI, machine learning, and industrial analytics for maintenance optimization. It covers techniques such as anomaly detection, condition monitoring, and fault prediction using sensor data. The book also discusses data preprocessing, model training, and real-time decision-making systems. By integrating theoretical foundations with practical implementations, it provides engineers and researchers with the tools to build intelligent and reliable manufacturing systems.
Dr. Sree Ram Nimmagadda is a distinguished Associate Professor in the Department of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation (KLEF), Guntur, Andhra Pradesh, India. He has been an integral part of the institution for the past ten years, contributing significantly to both academic and research excellence. Dr. Nimmagadda earned his Doctor of Philosophy (Ph.D.) in Data Mining and Machine Learning from the Jawaharlal Nehru Technological University, Kakinada (JNTUK), where his doctoral research focused on developing intelligent computational models and algorithms to address complex data-driven problems.With a professional career spanning over twenty-one years in teaching, research, and academic leadership, Dr. Nimmagadda has made substantial contributions to the domains of data mining, machine learning, artificial intelligence, and big data analytics. His work emphasizes the design and implementation of advanced learning systems, predictive models, and knowledge discovery frameworks that enhance decision-making across diverse applications. He has been actively involved in guiding undergraduate, postgraduate, and doctoral students, promoting interdisciplinary research, and fostering innovation in emerging areas of computer science
