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Unlike traditional PdM books that dive deeply into a single technique, this guide covers Extended PdM Methodologies in one practical volume. It explores not only classical methods such as vibration, thermal, and oil analysis, but also advanced and less common approaches including motor current analysis, wear debris, partial discharge, pressure, and efficiency monitoring.
Rather than replacing specialist handbooks, this book focuses on how to integrate multiple PdM techniques with sensors, industrial data, and AI/ML tools to design Industry 4.0-ready predictive maintenance systems.
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 vibration, oil, thermal, and acoustic monitoring in AI-enhanced workflows. Incorporate advanced methods such as motor current, wear debris, partial discharge, pressure, and efficiency monitoring. 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 practical insights, this book serves as a broad, integrative roadmap for engineers, reliability professionals, and Industry 4.0 practitioners looking to harness AI-driven predictive maintenance across industries such as energy, aviation, automotive, petrochemicals, and manufacturing.
