Cargando…
Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of them require sufficient historical labeled data to tr...
Autores principales: | Shao, Xiaorui, Kim, Chang-Soo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185426/ https://www.ncbi.nlm.nih.gov/pubmed/35684777 http://dx.doi.org/10.3390/s22114156 |
Ejemplares similares
-
CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis
por: Chung, Ching-Che, et al.
Publicado: (2023) -
Rolling Bearing Fault Diagnosis Using Multi-Sensor Data Fusion Based on 1D-CNN Model
por: Wang, Hongwei, et al.
Publicado: (2022) -
Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
por: Wang, Xiaodong, et al.
Publicado: (2020) -
Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data
por: Chao, Ko-Chieh, 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)