AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current

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.