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.