Cargando…
Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network
The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situations, the majority of fault samples have the issue of...
Autores principales: | Liu, Jianhua, Yang, Haonan, He, Jing, Sheng, Zhenwen, Chen, Shou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986379/ https://www.ncbi.nlm.nih.gov/pubmed/35401722 http://dx.doi.org/10.1155/2022/1875011 |
Ejemplares similares
-
Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis
por: He, Jing, et al.
Publicado: (2022) -
An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE
por: Duan, Feng, et al.
Publicado: (2022) -
CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things
por: Gui, Zhenwen, et al.
Publicado: (2023) -
A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset
por: Tang, Hongtao, et al.
Publicado: (2021) -
Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis
por: Jiang, Wen, et al.
Publicado: (2016)