Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges

by Kim Phuc Tran (Editor)

Overview

This book is a comprehensive exploration of the latest theoretical research, technological advancements, and real—world applications of artificial intelligence (AI) for safety and reliability engineering.

Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision—makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.

Product Details

ISBN-13:	9783031714948
Publisher:	Springer Nature Switzerland
Publication date:	09/29/2024
Series:	Springer Series in Reliability Engineering
Edition description:	2024
Pages:	199
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Kim Phuc Tran is a Senior Associate Professor (Maître de Conférences HDR, equivalent to UK Reader) of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France.

Table of Contents

Introduction to Artificial Intelligence for Safety and Reliability Engineering.— Artificial Intelligence for Safety and Reliability Engineering in Industry 5.0 Methods, Applications and Challenges.— 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.— Inverse Reinforcement Learning for Predictive Maintenance.— Reliability and Risk Assessment with Explainable Artificial Intelligence.— An Anomaly Detection Framework for Safety and Reliability Engineering.— Wearable Technology for Workplace Safety with Embedded Artificial Intelligence.— Safety and Reliability of Artificial Intelligence systems.— Physics—informed machine learning for reliability and systems safety applications.