AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Effic

Unlike traditional PdM books, this guide covers Extended PdM Methodologies - not only vibration, thermal, and oil analysis, but also rare and advanced techniques such as motor current analysis, wear debris, partial discharge, pressure, and efficiency analysis.

This book provides a comprehensive yet practical roadmap for engineers, reliability professionals, and Industry 4.0 practitioners who want to harness Artificial Intelligence for predictive maintenance.

Inside, you will learn how to:

• Collect, preprocess, and analyze industrial data from IoT, SCADA, and sensors.
• Apply AI and ML models (Random Forest, LSTM, CNN, Autoencoders) to predict equipment failures.
• Use classical PdM methodologies such as vibration, oil, thermal, and acoustic monitoring.
• Implement rare and advanced techniques (motor current, wear debris, partial discharge, pressure, efficiency).
• Build predictive workflows from model training to deployment and monitoring.
• Evaluate ROI and integrate PdM into Industry 4.0 ecosystems (Digital Twin, Cloud/Edge, 5G).

With a balance of theory, case studies, and hands-on insights, this book is your complete toolkit to design, implement, and optimize AI-driven predictive maintenance strategies across industries including energy, aviation, automotive, petrochemicals, and manufacturing.