A Multi-Adversarial Joint Distribution Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions

Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. T...

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Bibliografische Detailangaben

Hauptverfasser:	Zhichao Cui, Hui Cao, Zeren Ai, Jihui Wang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2023-09-01
Schriftenreihe:	Applied Sciences
Schlagworte:	intelligent diagnosis multi-adversarial domain adaptation deep transfer learning rolling bearing
Online-Zugang:	https://www.mdpi.com/2076-3417/13/19/10606
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