Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

1. Auflage 2024
ISBN: 978-1-032-75237-2
Verlag: CRC Press

Buch, Englisch, 216 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 614 g

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.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Yan, Ruqiang

Zhao, Zhibin

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

1:Introduction and Background  Part I: Basic applications of deep learning enabled Intelligent Fault Diagnosis  2:Auto-encoders for Intelligent Fault Diagnosis  3:Deep Belief Networks for Intelligent Fault Diagnosis  4:Convolutional Neural Networks for Intelligent Fault Diagnosis  Part II: advanced topics of deep learning enabled Intelligent Fault Diagnosis  5:Data Augmentation for Intelligent Fault Diagnosis  6:Multi-sensor Fusion for Intelligent Fault Diagnosis  7: Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis  8: Neural Architecture Search for Intelligent Fault Diagnosis  9: Self-Supervised Learning (SSF) for Intelligent Fault Diagnosis  10: Reinforcement Learning for Intelligent Fault Diagnosis

Über Autor(innen)

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.