Human-Centered Explainable Anomaly Detection for Smart Manufacturing in Industry 5.0
Springer International Publishing (Verlag)
978-3-032-13656-5 (ISBN)
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This book systematically presents the application of Human-Centered Explainable Anomaly Detection (HCXAD) in Smart Manufacturing (SM). This book addresses HCXAD as an approach that places the human at the center of technology design, aiming to bridge the gap between Explainable AI (XAI) and its real-world impact. The book will also cover the applications of HCXAD in SM, including predictive maintenance, cybersecurity of Industrial Internet of Things (IIoT) systems, fault detection, and reliability analysis for manufacturing processes. It will introduce readers to the latest theoretical research, technological developments, and practical applications of HCXAD, addressing the current challenges and opportunities in Smart Manufacturing. Additionally, the book will provide ready-to-use algorithms for readers and practitioners, tailored to several potential HCXAD applications in SM. Case studies will be presented in each chapter to help readers and practitioners easily apply these tools to real-world Smart Manufacturing processes.
Dr. habil. Kim Phuc Tran is a Senior Associate Professor (Maître de Conférences HDR) at ENSAIT University of Lille, France, and Senior Researcher at the GEMTEX Laboratory. He also serves as Founding Director of the International Chair in Data Science & Explainable AI at Dong A University (Vietnam).
His research focuses on Industrial AI, Explainable and Federated Learning, Edge Intelligence, and Hybrid Modeling (Physics & Data), with applications spanning Smart Manufacturing, Healthcare, and Energy Systems.
He has authored over 75 international publications, edited several Springer volumes, and serves on editorial boards including IEEE Transactions on Intelligent Transportation Engineering Applications of Artificial Intelligence.
Introduction to Human-Centered Explainable Anomaly Detection for Smart Manufacturing in Industry 5.0.- Anomaly Detection for Catalyzing Operational Excellence in Complex Manufacturing Processes: A.- Survey and Perspective.- System Reliability: Inference for Common Cause Failure Model in Contexts of Missing Information.- .- Predictive maintenance enabled by a Light-Weight Federated Learning in Smart Manufacturing: Remaining Useful Lifetime Prediction.- Explainable Trustworthy, and Transparent Artificial Intelligence for Reliability Engineering and Safety Applications.- Human-Centered Explainable Anomaly Detection for Predictive Maintenance.- .- Reliability and Risk Assessment with Human-Centered Explainable Anomaly Detection.- An Human-Centered Explainable Anomaly Detection Framework for Safety and Reliability Engineering.- Wearable Technology for Workplace Safety with Human-Centered Explainable Anomaly Detection.- Safety and Reliability of Human-Centered Explainable Anomaly Detection systems.- Physics-informed machine learning for Human-Centered Explainable Anomaly Detection systems.
| Erscheint lt. Verlag | 2.3.2026 |
|---|---|
| Reihe/Serie | Springer Series in Reliability Engineering |
| Zusatzinfo | X, 190 p. 472 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik | |
| Technik ► Maschinenbau | |
| Schlagworte | Artificial Intelligence • Deep learning • Industry 50 • machine learning • Predictive Maintenance • Reinforcement Learning • Remaining Useful Lifetime Prediction • Safety and Reliability Engineering • Smart Condition Monitoring • smart manufacturing |
| ISBN-10 | 3-032-13656-3 / 3032136563 |
| ISBN-13 | 978-3-032-13656-5 / 9783032136565 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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