Intelligent Reliability and Maintainability of Energy Infrastructure Assets: Approaches, Case Studies, Mathematical Modelling, ... and Artificial Intelligence Methods: 473 - Rilegato

Li, He; Peng, Weiwen; Adumene, Sidum; Yazdi, Mohammad

Rilegato

ISBN 10: 3031299612 ISBN 13: 9783031299612

Casa editrice: Springer Nature, 2023

Riassunto

This book reviews and presents several advanced approaches to energy infrastructure assets' intelligent reliability and maintainability. Each introduced model provides case studies indicating high efficiency, robustness, and applicability, allowing readers to utilize them in their understudy intelligent reliability and maintainability of energy infrastructure assets domains.

The book begins by reviewing the state-of-the-art research on the reliability and maintainability of energy infrastructure assets and emphasizes the intelligent tools and methods proposed from a bibliometric and literature review point of view. It then progresses logically, dedicating a chapter to each approach, dynamic Bayesian modeling network, convolutional neural network model, global average pooling-based convolutional Siamese network, an integrated probabilistic model for the failure consequence assessment, and more.

This book interests professionals and researchers working in reliability and maintainability and postgraduate and undergraduate students studying intelligent reliability applications and energy infrastructure assets' maintainability.

Su questo libro

Dalla quarta di copertina

This book reviews and presents several advanced approaches to energy infrastructure assets' intelligent reliability and maintainability. Each introduced model provides case studies indicating high efficiency, robustness, and applicability, allowing readers to utilize them in their understudy intelligent reliability and maintainability of energy infrastructure assets domains.

The book begins by reviewing the state-of-the-art research on the reliability and maintainability of energy infrastructure assets and emphasizes the intelligent tools and methods proposed from a bibliometric and literature review point of view. It then progresses logically, dedicating a chapter to each approach, dynamic Bayesian modeling network, convolutional neural network model, global average pooling-based convolutional Siamese network, an integrated probabilistic model for the failure consequence assessment, and more.

This book interests professionals and researchers working in reliability and maintainability and postgraduate and undergraduate students studying intelligent reliability applications and energy infrastructure assets' maintainability.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 3031299612
• ISBN 13: 9783031299612
• Rilegatura: Copertina rigida
• Numero di pagine: 164
• Contatto del produttore: non disponibile
• Persona responsabile: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Germania