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Intelligent Reliability and Maintainability of Energy Infrastructure Assets: Approaches, Case Studies, Mathematical Modelling, ... in Systems, Decision and Control, 473) - Hardcover
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.
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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.
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2023
- ISBN 10 3031299612
- ISBN 13 9783031299612
- BindingHardcover
- LanguageEnglish
- Number of pages161
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Intelligent Reliability and Maintainability of Energy Infrastructure Assets
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Springer Nature Switzerland, Springer International Publishing Mai 2023, 2023
ISBN 10: 3031299612
ISBN 13: 9783031299612
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch. Seller Inventory # 9783031299612
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Intelligent Reliability and Maintainability of Energy Infrastructure Assets
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 reade. Seller Inventory # 829430202
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Intelligent Reliability and Maintainability of Energy Infrastructure Assets
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Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031299612
ISBN 13: 9783031299612
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783031299612
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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
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