Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples

Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled samples are required to support the algorithm of CNNs, a...

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Detalles Bibliográficos
Autores principales: Wei, Meirong, Liu, Yan, Zhang, Tao, Wang, Ze, Zhu, Jiaming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749802/
https://www.ncbi.nlm.nih.gov/pubmed/35009734
http://dx.doi.org/10.3390/s22010192

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749802/
https://www.ncbi.nlm.nih.gov/pubmed/35009734
http://dx.doi.org/10.3390/s22010192

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