Buch, Englisch, 312 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Innovations in Intelligent Internet of Everything (IoE)
Buch, Englisch, 312 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Innovations in Intelligent Internet of Everything (IoE)
ISBN: 978-1-032-75948-7
Verlag: Taylor & Francis
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.
Zielgruppe
Postgraduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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-Classification 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
