Deep neural networks-enabled intelligent fault diagnosis of mechanical systems / Ruqiang Yan and Zhibin Zhao.
- 作者： Yan, Ruqiang.
- 其他作者：
- 出版： Boca Raton : CRC Press/Taylor & Francis Group 2024.
- 主題： Fault location (Engineering)--Data processing. , Deep learning (Machine learning)--Industrial applications.
- 版本：1st ed.
- ISBN： 9781032752372 (hbk.) :: GBP81.99 、 9781032757247 (pbk.)
- 書目註：Includes bibliographical references and index.
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讀者標籤：
- 系統號： 005302020 | 機讀編目格式
館藏資訊
摘要註
"The book aims to highlight the potential of Deep Learning (DL)-enabled methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionise the nature of IFD, the book contributes to improved efficiency, safety and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning"--