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.
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