03835nam a22005535i 4500 978-3-031-71495-5 DE-He213 20251030113112.0 cr nn 008mamaa 240928s2024 sz | s |||| 0|eng d 9783031714955 978-3-031-71495-5 10.1007/978-3-031-71495-5 doi T55.4-60.8 TGP bicssc TEC009060 bisacsh TGP thema Artificial Intelligence for Safety and Reliability Engineering [electronic resource] : Methods, Applications, and Challenges / edited by Kim Phuc Tran. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. V, 199 p. 56 illus., 48 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Series in Reliability Engineering, 2196-999X 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. 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. . Industrial engineering. Production engineering. Artificial intelligence. 58441 Production management. 67141 Industrial and Production Engineering. Artificial Intelligence. 58441 Production. 67142 Tran, Kim Phuc. editor. (orcid)0000-0002-6005-1497 https://orcid.org/0000-0002-6005-1497 edt http://id.loc.gov/vocabulary/relators/edt 73219 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031714948 Printed edition: 9783031714962 Printed edition: 9783031714979 Springer Series in Reliability Engineering, 2196-999X 71674 https://doi.org/10.1007/978-3-031-71495-5 ZDB-2-ENG ZDB-2-SXE 63802 63802