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.
Advances in Intelligent Reliability and Maintainability of Energy Infrastructure Assets.- Cutting Edge Research Topics on System Safety, Reliability, Maintainability, and Resilience of Energy-critical Infrastructures.- Operation management of critical energy infrastructure: A sustainable approach.- An improved LeNet-5 convolutional neural network supporting condition-based maintenance and fault diagnosis of bearings.- Using Global Average Pooling Convolutional Siamese Networks for fault diagnosis of planetary gearboxes.- Advances in failure prediction of subsea components considering complex dependencies.- An intelligent cost-based consequence model for the offshore system in harsh environments.- A Sustainable Circular Economy in Energy Infrastructure: Application of Supercritical Water Gasification System.- Attention towards Energy infrastructures, Challenges and Solutions.
