Cargando…
Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions
In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have...
Autores principales: | Jang, Gye-Bong, Cho, Sung-Bae |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922640/ https://www.ncbi.nlm.nih.gov/pubmed/33670547 http://dx.doi.org/10.3390/s21041417 |
Ejemplares similares
-
A Novel Method for Fault Diagnosis of Rotating Machinery
por: Tang, Meng, et al.
Publicado: (2022) -
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
por: Li, Chuan, et al.
Publicado: (2016) -
Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
por: Inyang, Udeme Ibanga, et al.
Publicado: (2023) -
Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification
por: Mochammad, Solichin, et al.
Publicado: (2022) -
An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions
por: He, Wangpeng, et al.
Publicado: (2022)