AI and Machine Learning for Mechanical and Electrical Engineering (Hardcover)

T. Rajasanthosh Kumar

ISBN 10: 1032759488 / ISBN 13: 9781032759487

Published by Taylor & Francis Ltd, London, 2025

About this Item

Hardcover. Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following:Developing a smart algorithm to integrate fault detection and classificationAlgorithms to investigate different testing scenarios for various anomalies in electric motorsData fusion to detect and assess electromechanical damageNeural networks for rolling bearing fault diagnosisEvolutionary algorithms to optimize deep learning models for water industry forecastsAI-based anomaly detection and root-cause analysisAn overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future. The book examines issues involved in the transition from traditional mechanical and electrical engineering and their management systems to the new engineering paradigms created by the application of smart systems. It covers applications, methods to transition to smart engineering and management, and associated ethical implications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9781032759487

Bibliographic Details

• Title: AI and Machine Learning for Mechanical and Electrical Engineering (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2025
• Language: English
• ISBN 10: 1032759488
• ISBN 13: 9781032759487
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following:

• Developing a smart algorithm to integrate fault detection and classification
• Algorithms to investigate different testing scenarios for various anomalies in electric motors
• Data fusion to detect and assess electromechanical damage
• Neural networks for rolling bearing fault diagnosis
• Evolutionary algorithms to optimize deep learning models for water industry forecasts
• AI-based anomaly detection and root-cause analysis

An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

About the Author

Dr. T. Rajasanthosh Kumar is an associate professor of the Department of Mechanical Engineering at Oriental Institute of Science and Technology, Bhopal, India.

Dr. Surendra Reddy Vinta is an associate professor of the School of Computer Science and Engineering at VIT-AP University, Amaravati, India.

Dr. Sagar Dhanraj Pande is head of the School of Engineering and Technology at Pimpri Chinchwad University, Pune, Maharashtra, India.

Dr. Aditya Khamparia is an assistant professor and coordinator of the Department of Computer Science at Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India.