Ruqiang Yan, Zhibin Zhao

Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

• Gebundenes Buch

Produktbeschreibung

The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions.

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 232
• Erscheinungstermin: 6. Juni 2024
• Abmessung: 254mm x 178mm
• ISBN-13: 9781032752372
• ISBN-10: 1032752378
• Artikelnr.: 69792936

Autorenporträt

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.

Inhaltsangabe

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