A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

Intelligent fault diagnosis methods based on deep learning have achieved much progress in recent years. However, there are two major factors causing serious degradation of the performance of these algorithms in real industrial applications, i.e., limited labeled training data and complex working con...

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Detalles Bibliográficos
Autores principales: He, Qiuchen, Li, Shaobo, Li, Chuanjiang, Zhang, Junxing, Zhang, Ansi, Zhou, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270151/
https://www.ncbi.nlm.nih.gov/pubmed/35814590
http://dx.doi.org/10.1155/2022/3024590

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270151/
https://www.ncbi.nlm.nih.gov/pubmed/35814590
http://dx.doi.org/10.1155/2022/3024590

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