Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
暫譯: 深度神經網絡驅動的機械系統智能故障診斷
Yan, Ruqiang, Zhao, Zhibin
- 出版商: CRC
- 出版日期: 2026-06-21
- 售價: $2,620
- 貴賓價: 9.5 折 $2,489
- 語言: 英文
- 頁數: 206
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032757248
- ISBN-13: 9781032757247
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
商品描述
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 revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems 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.
商品描述(中文翻譯)
本書旨在突顯深度學習（DL）驅動的方法在智能故障診斷（IFD）中的潛力，以及它們的好處和貢獻。
作者首先介紹了DL驅動的IFD的基本應用，包括自編碼器（auto-encoders）、深度信念網絡（deep belief networks）和卷積神經網絡（convolutional neural networks）。本書還探討了DL驅動的IFD的進階主題，如數據增強（data augmentation）、多傳感器融合（multi-sensor fusion）、無監督深度轉移學習（unsupervised deep transfer learning）、神經架構搜索（neural architecture search）、自我監督學習（self-supervised learning）和強化學習（reinforcement learning）。本書《深度神經網絡驅動的機械系統智能故障診斷》旨在徹底改變IFD的性質，為各種工業領域的機械系統提高效率、安全性和可靠性做出貢獻。
本書將吸引智能故障診斷、預測與健康管理以及深度學習領域的學術研究者、實務工作者和學生。
作者簡介
Ruqiang Yan is a professor at the School of Mechanical Engineering, Xi'an Jiaotong University. His research interests include data analytics, AI, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems.
Zhibin Zhao is an assistant professor at the School of Mechanical Engineering, Xi'an Jiaotong University. His research interests include sparse signal processing and machine learning, especially deep learning for machine fault detection, diagnosis, and prognosis.
作者簡介(中文翻譯)
嚴如強是西安交通大學機械工程學院的教授。他的研究興趣包括數據分析、人工智慧，以及用於大型、複雜、動態系統的狀態監測和健康診斷的能源效率感測和感測器網絡。
趙志彬是西安交通大學機械工程學院的助理教授。他的研究興趣包括稀疏信號處理和機器學習，特別是用於機器故障檢測、診斷和預測的深度學習。
