A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions

Most current intelligent fault diagnosis models, dependent on specific working condition data for training, cannot effectively diagnose faults in unknown working conditions without data. Zero-shot learning (ZSL) can identify samples unseen during the training phase and has now been applied to the fi...

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Vydané v:	Engineering applications of artificial intelligence Ročník 136; s. 109020
Hlavní autori:	Shang, Zhiwu, Tang, Lutai, Pan, Cailu, Cheng, Hongchuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2024
Predmet:	Autoencoder Fault diagnosis Semantic learning Transfer learning Zero-shot learning Fault diagnosis Zero-shot learning Autoencoder Transfer learning Semantic learning
ISSN:	0952-1976
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