Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-s...

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Detalles Bibliográficos
Autores principales: Yang, Zheng, Chen, Fei, Xu, Binbin, Ma, Boquan, Qu, Zege, Zhou, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422390/
https://www.ncbi.nlm.nih.gov/pubmed/37571734
http://dx.doi.org/10.3390/s23156951

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422390/
https://www.ncbi.nlm.nih.gov/pubmed/37571734
http://dx.doi.org/10.3390/s23156951

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