Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis
Description
Unlike traditional PdM books that focus on a single technique, this guide provides a practical overview of Extended Predictive Maintenance (PdM) methodologies in one volume. It covers both classical approaches—such as vibration, thermal, acoustic, and oil analysis—and advanced techniques including motor current analysis, wear debris monitoring, 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.
You'll learn how to collect and analyze industrial data, apply AI and machine learning models, integrate multiple condition-monitoring methods, and build Industry 4.0–ready predictive maintenance systems. Covering topics from model development and deployment to Digital Twins, Cloud/Edge computing, and ROI evaluation, this book offers a practical roadmap for engineers, reliability professionals, and Industry 4.0 practitioners seeking to implement AI-driven maintenance strategies across modern industries.
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‘AI for Predictive Maintenance in Industry 4.0 - Soliman, Mohammed Hamed Ahmed’.
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