Overview

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

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

Key Themes

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.