Artificial Intelligence for Safety and Reliability Engineering [electronic resource] : Methods, Applications, and Challenges / edited by Kim Phuc Tran.

Colaborador(es): Tran, Kim Phuc [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Series in Reliability EngineeringEditor: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: V, 199 p. 56 illus., 48 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031714955Tema(s): Industrial engineering | Production engineering | Artificial intelligence | Production management | Industrial and Production Engineering | Artificial Intelligence | ProductionFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 670 Clasificación LoC:T55.4-60.8Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer Nature eBookResumen: 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. .
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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. .

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