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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.
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.
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