Articles liés à AI for Predictive Maintenance in Industry 4.0: Extended...

AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure,... - Couverture souple

Livre 9 sur 9: Condition Monitoring & Predictive Maintenance Series

Soliman, Mohammed Hamed Ahmed

 
9789403906454: AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure,...
Couverture souple
ISBN 10 : 9403906456 ISBN 13 : 9789403906454
Editeur : Bookmundo, 2026

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Internationally Recognized Lean Expert Dr. MHA Soliman is a Lecturer in Industrial Engineering and Management Systems at the American University in Cairo, an Executive Advisor and a member of the Advisory Committee of the IEOM International Society. He brings declines of academic and consulting experience and is recognized as a leading expert in organizational performance, institutional transformation for total quality systems, and the quality of public services. He has authorized over 100 peer-reviewed scientific publications and works as an executive consultant, author, and researcher specializing in management, continuous improvement, and service development.

À propos de la quatrième de couverture

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Bookmundo
Date d'édition
2026
Langue
anglais
ISBN 10 
9403906456
ISBN 13 
9789403906454
Reliure
Broché
Numéro d'édition
1
Nombre de pages
261
Coordonnées du fabricant
Bookmundo
info@bookmundo.com

Delftsestraat 33
Rotterdam
3013AE
Pays-Bas