AI and Machine Learning for Mechanical and Electrical Engineering
1st Edition
Published on 6. October 2025
Book
Hardback
321 pages
978-1-032-75948-7 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Postgraduate
Illustrations
121 s/w Abbildungen, 121 s/w Zeichnungen
121 Line drawings, black and white; 121 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 22 mm
Weight
661 gr
ISBN-13
978-1-032-75948-7 (9781032759487)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
T. Rajasanthosh Kumar | Surendra Reddy Vinta | Sagar Dhanraj Pande
AI and Machine Learning for Mechanical and Electrical Engineering
E-Book
10/2025
1st Edition
Auerbach
€225.99
Available for download
T. Rajasanthosh Kumar | Surendra Reddy Vinta | Sagar Dhanraj Pande
AI and Machine Learning for Mechanical and Electrical Engineering
E-Book
10/2025
1st Edition
Auerbach
€225.99
Available for download
Persons
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.
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.
Content
1. Development of a Smart Algorithm to Integrate Fault Detection and Classification of End-to-End Monitoring of Autonomous Transfer Vehicles 2. Data Science and ML Algorithms to Investigate Different Testing Scenarios for Various Anomalies in Driven Electric Motor 3. A Data Fusion Technique to Detect and Assess Electromechanical Damage 4. AI: Classifications and Protection of Smart Grid Systems 5. An Artificial Intelligence-Based Solar Radiation Prophesy Model for Green Energy Utilization in Energy Management System 6. Two-Channel Convolutional Neural Networks for Rolling Bearing Fault Diagnosis in Unbalanced Datasets 7. The Implementation of Artificial Intelligence for Auto Gearbox Failure Detection 8. Evolutionary Algorithms to Optimise Deep Learning Models for Water Industry Forecasts 9. Artificial Intelligence Anomaly Detection and Root-Cause Analysis 10. Artificial Intelligence and Internet of Things-Based Intelligent Scheduling for Load Distribution in Power Grids 11. Coordinated Response Strategies: Swarm Robotics for Crisis Management 12. Smart Farming and Human-Bioinformatics Systems Based on IoT and Sensor Devices 13. Machine Learning Techniques Applied to Predictive Maintenance: A Review 14. Optimization of Parameters During Tribological Investigations on Azadirachta Indica Based Bio-Composites 15. ANFIS Modelling Study on Surface Water Analysis 16. WSN-Based Optimal Crude Oil Storage Health Monitoring Framework 17. Cybersecurity Education Gamification: A Current Review and Research Agenda 18. Artificial Intelligence and Cybersecurity in 6G Wireless Networks