A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and class...
Guardado en:
Autores principales: | Bin Han, Hui Zhang, Ming Sun, Fengtong Wu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c109bc54af76403fa12886d990ca753b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
por: Shih-Lin Lin
Publicado: (2021) -
Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN
por: Jiajun He, et al.
Publicado: (2021) -
Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
por: Yuanyuan Yang, et al.
Publicado: (2021) -
Intelligent Fault Diagnosis Method of Wind Turbines Planetary Gearboxes Based on a Multi-Scale Dense Fusion Network
por: Xinghua Huang, et al.
Publicado: (2021) -
Towards the Integration of Reliability and Security Mechanisms to Enhance the Fault Resilience of Neural Networks
por: Nikolaos Ioannis Deligiannis, et al.
Publicado: (2021)