Human-Centered Explainable Anomaly Detection For Smart Manufacturing In Industry 5.0: Theories, Applications And Case Studies

Tran, Kim Phuc (Editor)

ISBN 10: 3032136563 / ISBN 13: 9783032136565

Published by Springer Nature Switzerland Ag, 2026

About this Item

207 pages. 6.30x0.67x9.49 inches. In Stock.

Seller Inventory # x-3032136563

Bibliographic Details

• Title: Human-Centered Explainable Anomaly Detection For Smart Manufacturing In Industry 5.0: Theories, Applications And Case Studies
• Publisher: Springer Nature Switzerland Ag
• Publication Date: 2026
• Language: English
• ISBN 10: 3032136563
• ISBN 13: 9783032136565
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.45 kilograms

About this title

Synopsis

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.

About the Author

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.