Book

Description

In today’s industrial landscape, machines generate more data than ever before but data alone does not prevent failure. What matters is how that data is interpreted, validated, and translated into effective maintenance decisions. Industrial Machinery Health Management with Generative Artificial Intelligence presents a practical, engineer-focused approach to modern Prognostics and Health Management (PHM). This book bridges the gap between traditional condition monitoring and emerging AI capabilities, showing how field evidence, engineering reasoning, and intelligent tools can work together to improve reliability and reduce uncertainty. Through structured workflows, real-world case studies, and actionable templates, readers learn how to: Interpret sensor data in real operating contexts Validate anomalies using physical inspection and diagnostic tools Apply Generative AI responsibly for analysis, documentation, and decision support Convert insights into clear maintenance actions and measurable outcomes Rather than treating AI as a replacement for engineering judgment, this book emphasizes disciplined, evidence-based decision-making where human expertise remains central and AI acts as a powerful assistant. Designed for maintenance engineers, reliability professionals, and industrial practitioners, this guide delivers a repeatable framework for turning machine data into confident, auditable, and effective action.