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Synopsis
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.
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About the Author
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.
"About this title" may belong to another edition of this title.
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems (Paperback)
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Taylor & Francis Ltd, London, 2026
ISBN 10: 1032757248
ISBN 13: 9781032757247
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Paperback. Condition: new. Paperback. 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. The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032757247
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems (Paperback)
Published by
Taylor & Francis Ltd, London, 2026
ISBN 10: 1032757248
ISBN 13: 9781032757247
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Paperback. Condition: new. Paperback. 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. The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781032757247
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis Of Mechanical Systems
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems (Paperback)
Published by
Taylor & Francis Ltd, London, 2026
ISBN 10: 1032757248
ISBN 13: 9781032757247
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Paperback. Condition: new. Paperback. 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. The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781032757247
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
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Condition: New. 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. Seller Inventory # 3236057093
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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
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Taschenbuch. Condition: Neu. Neuware - 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. Seller Inventory # 9781032757247