Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems (ePub)
(Sprache: Englisch)
The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions.
Erscheint am 06.06.2024
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Produktdetails
Produktinformationen zu „Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems (ePub)“
The book aims to highlight the potential of Deep Learning (DL)-based methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions.
Autoren-Porträt von Ruqiang Yan, Zhibin Zhao
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.
Bibliographische Angaben
- Autoren: Ruqiang Yan , Zhibin Zhao
- 2024, 1. Auflage, 232 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1040026613
- ISBN-13: 9781040026618
- Erscheinungsdatum: 06.06.2024
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 7.15 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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