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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. AI for Predictive Maintenance in Industry 4.0 | Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis | Mohammed Hamed Ahmed Soliman | Taschenbuch | Englisch | 2026 | Personal Lean Publications | EAN 9789403906454 | Verantwortliche Person für die EU: Bookmundo, Delftsestraat 33, 3013 AE ROTTERDAM, NIEDERLANDE, info[at]bookmundo[dot]com | Anbieter: preigu. Artikel-Nr. 135609538
Anzahl: 5 verfügbar