Intelligent Reliability and Maintainability of Energy Infrastructure Assets: Approaches, Case Studies, Mathematical Modelling, Advanced-decision-making Tools, Operation Research, Progressive Machine Learning and Artificial Inte by Li, He/ Peng, Weiwen/ Adumene, Sidum/ Yazdi, Mohammad: Brand New Hardcover (2023)

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Bibliographic Details

Title: Intelligent Reliability and Maintainability of Energy Infrastructure Assets: Approaches, Case Studies, Mathematical Modelling, Advanced-decision-making Tools, Operation Research, Progressive Machine Learning and Artificial Inte
Publisher: Springer Nature
Publication Date: 2023
Language: English
ISBN 10: 3031299612
ISBN 13: 9783031299612
Binding: Hardcover
Condition: Brand New

About this title

Synopsis

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.

From the Back Cover

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.

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