Atsiliepimai
Aprašymas
This book explores a wide range of topics in reliability and maintenance engineering, including fault diagnosis, prognostics, condition monitoring, risk assessment, optimization, and decision-making. By leveraging computational intelligence techniques-such as artificial intelligence, machine learning, evolutionary algorithms, fuzzy logic, and neural networks-it presents innovative solutions for improving the dependability and lifecycle performance of critical systems and assets.
Its primary aim is to bridge the gap between theory and real-world practice, offering insights that combine academic research with industrial applications. Researchers, practitioners, and students in reliability engineering, maintenance management, industrial engineering, and computational intelligence will find a valuable resource for strengthening their knowledge and skill set in data-driven reliability improvement.
Special features include practical case studies, hands-on examples, and implementation strategies that demonstrate how intelligent methods can be applied across real maintenance scenarios. The book adopts a multidisciplinary perspective, drawing on expertise from multiple domains to provide a holistic understanding of reliability challenges and smart solutions.
This book explores a wide range of topics in reliability and maintenance engineering, including fault diagnosis, prognostics, condition monitoring, risk assessment, optimization, and decision-making. By leveraging computational intelligence techniques-such as artificial intelligence, machine learning, evolutionary algorithms, fuzzy logic, and neural networks-it presents innovative solutions for improving the dependability and lifecycle performance of critical systems and assets.
Its primary aim is to bridge the gap between theory and real-world practice, offering insights that combine academic research with industrial applications. Researchers, practitioners, and students in reliability engineering, maintenance management, industrial engineering, and computational intelligence will find a valuable resource for strengthening their knowledge and skill set in data-driven reliability improvement.
Special features include practical case studies, hands-on examples, and implementation strategies that demonstrate how intelligent methods can be applied across real maintenance scenarios. The book adopts a multidisciplinary perspective, drawing on expertise from multiple domains to provide a holistic understanding of reliability challenges and smart solutions.
Atsiliepimai