
AI and Machine Learning for Mechanical and Electrical Engineering
Description
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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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Persons
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
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File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
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