Cargando…
One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions
Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work u...
Autores principales: | Wang, Kai, Zhao, Wei, Xu, Aidong, Zeng, Peng, Yang, Shunkun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660602/ https://www.ncbi.nlm.nih.gov/pubmed/33114173 http://dx.doi.org/10.3390/s20216039 |
Ejemplares similares
-
Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN
por: He, Jiajun, et al.
Publicado: (2021) -
An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis
por: Xu, Meng, et al.
Publicado: (2023) -
A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions
por: Zhang, Ruixin, et al.
Publicado: (2022) -
Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing
por: Dong, Kaitai, et al.
Publicado: (2023) -
Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions
por: Xue, Lang, et al.
Publicado: (2017)