AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

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.

1148120859
AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

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.

15.99 In Stock
AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

by Mohammed Hamed Ahmed Soliman
AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

AI for Predictive Maintenance in Industry 4.0: Extended PdM Methodologies: From Vibration & Thermal to Motor Current, Wear Debris, Pressure, and Efficiency Analysis

by Mohammed Hamed Ahmed Soliman

Overview

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.

Product Details

ISBN-13: 9798231356539
Publisher: Personal-Lean.Org
Publication date: 08/27/2025
Pages: 258
Product dimensions: 5.50(w) x 8.50(h) x 0.58(d)

About the Author

Dr. Mohammed Hamed Ahmed Soliman is an internationally recognized Lean expert, author, and university lecturer. He has published over 100 books and articles on Lean thinking, quality systems, and industrial excellence. He currently teaches 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, and consults for global organizations across manufacturing, public services, and education.

With nearly two decades of academic and professional experience, Dr. Soliman has trained professionals across the Middle East, including engagements with Princess Nourah University in Saudi Arabia and Vale Oman Pelletizing Company. He has designed and delivered over 60 leadership and technical development programs, helping organizations build a culture of continuous improvement and operational excellence.

Earlier in his career, he worked in various industrial sectors including crystal-glass manufacturing, fertilizers, and chemicals, while educating teams on the Toyota Production System. He has led numerous lean transformation projects, delivering measurable results and uncovering substantial cost savings by targeting waste across production and service environments.

His lectures and training materials have reached over 200,000 learners via SlideShare, and his research is ranked among the most downloaded papers on the Social Science Research Network (SSRN) by Elsevier.

Dr. Soliman holds a BSc in Engineering, a master's in Quality Management, and postgraduate degrees in Industrial Engineering and Engineering Management. He also holds certifications in quality, cost, and operations management. He is a member of the Institute of Industrial and Systems Engineers (IISE) and the Society for Engineering and Management Systems (SEMS).

His insights have been featured in SAGE Publications, Industrial Management, Lean Thinking, and other peer-reviewed platforms.

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