AI and Machine Learning for Mechanical and Electrical Engineering
by T. Rajasanthosh Kumar, Surendra Reddy Vinta, Sagar Dhanraj Pande, Aditya Khamparia
1st Edition
Publisher: Auerbach Publications
Book Details
| Print ISBN | 9781032759487 |
| eText ISBN | 9781040403259 |
| Publisher | Auerbach Publications |
| Publishing Year | 2025 |
| Edition | 1st Edition |
| Language | English |
| Pages | 329 |
AI and Machine Learning for Mechanical and Electrical Engineering, 1st Edition, is a book that examines how artificial intelligence and machine learning are applied across mechanical engineering, electrical systems, and operational management. The publication provides analytical frameworks intended to support technical evaluation and monitoring across complex physical and digital engineering systems.
The text details specialized smart algorithms designed to assist end-to-end fault detection and classification within autonomous transfer vehicles. Additional topics cover predictive solar radiation forecasting models structured for green energy utilization in broader energy management systems, as well as computational procedures focused on auto gearbox failure detection.
Edited by T. Rajasanthosh Kumar, Surendra Reddy Vinta, Sagar Dhanraj Pande, and Aditya Khamparia, this 329-page volume delivers targeted resources for engineers. Published in 2025 by Auerbach Publications, the work offers analytical guidance for technical professionals navigating modern system integrations.
Table of Contents
Chapter 1: Development of a Smart Algorithm to Integrate Fault Detection and Classification of End-to-End Monitoring of Autonomous Transfer Vehicles
Chapter 2: Data Science and ML Algorithms to Investigate Different Testing Scenarios for Various Anomalies in Driven Electric Motor
Chapter 3: A Data Fusion Technique to Detect and Assess Electromechanical Damage
Chapter 4: AI: Classifications and Protection of the Smart Grid Systems
Chapter 5: An Artificial Intelligence-Based Solar Radiation Prophesy Model for Green Energy Utilisation in the Energy Management System
Chapter 6: Two-Channel Convolutional Neural Networks for Rolling Bearing Fault Diagnosis in Unbalanced Datasets
Chapter 7: The Implementation of Artificial Intelligence for Auto Gearbox Failure Detection
Chapter 8: Evolutionary Algorithms to Optimise Deep Learning Model for Water Industry Forecasts
Chapter 9: Artificial Intelligence Anomaly Detection and Root Cause Analysis
Chapter 10: Artificial Intelligence and Internet of Things-Based Intelligent Scheduling for Load Distribution in Power Grids
Chapter 11: Coordinated Response Strategies: Swarm Robotics for Crisis Management
Chapter 12: Smart Farming and Human Bioinformatics Systems Based on IoT and Sensor Devices
Chapter 13: Machine Learning Techniques Applied in Predictive Maintenance: A Review
Chapter 14: Optimization of Parameters During Tribological Investigations on Azadirachta indica-Based Bio-Composites
Chapter 15: ANFIS Modelling Study on Surface Wat
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