Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems - Couverture souple

Yan, Ruqiang; Zhao, Zhibin

Couverture souple

ISBN 10 : 1032757248 ISBN 13 : 9781032757247

Editeur : CRC Press, 2026

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.

A propos du livre

À propos de l'auteur

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.

Détails de l'édition

• Éditeur: CRC Press
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 1032757248
• ISBN 13: 9781032757247
• Reliure: Broché
• Nombre de pages: 218
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Allemagne