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.
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.
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.
