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.
Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how 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. Highlights include:
- 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.
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
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 an assistant professor (senior grade) at VIT-AP University, Amaravati, 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.
