Bearing Fault Detection based on Few-Shot Learning in Siamese Network

This paper executes bearing fault diagnosis with little data through few-shot learning. Recently, deep learning-based fault diagnosis methods have achieved promising results. In previous studies, fault diagnosis requires numerous training samples. However, in manufacturing, it is not possible to obt...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:WSEAS TRANSACTIONS ON SYSTEMS 2022-12, Vol.21, p.276-282
Hauptverfasser: Lee, Daehwan, Jeong, Jongpil
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper executes bearing fault diagnosis with little data through few-shot learning. Recently, deep learning-based fault diagnosis methods have achieved promising results. In previous studies, fault diagnosis requires numerous training samples. However, in manufacturing, it is not possible to obtain sufficient training samples for all failure types under all working conditions. In this work, we propose a Few shot learning-based rolling bearing fault diagnosis that can effectively learn with limited data. Our model is based on the siamese network, which learns to use the same or different class of sample pairs.
ISSN:1109-2777
2224-2678
DOI:10.37394/23202.2022.21.30