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Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges 2024 ed. [Kietas viršelis]

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  • Formatas: Hardback, 199 pages, aukštis x plotis: 235x155 mm, 48 Illustrations, color; 8 Illustrations, black and white; V, 199 p. 56 illus., 48 illus. in color., 1 Hardback
  • Serija: Springer Series in Reliability Engineering
  • Išleidimo metai: 29-Sep-2024
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031714946
  • ISBN-13: 9783031714948
Kitos knygos pagal šią temą:
Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges 2024 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 199 pages, aukštis x plotis: 235x155 mm, 48 Illustrations, color; 8 Illustrations, black and white; V, 199 p. 56 illus., 48 illus. in color., 1 Hardback
  • Serija: Springer Series in Reliability Engineering
  • Išleidimo metai: 29-Sep-2024
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031714946
  • ISBN-13: 9783031714948
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031714948.html
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.

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.

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