Cargando…
Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis
With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of...
Autores principales: | You, Keshun, Qiu, Guangqi, Gu, Yingkui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699405/ https://www.ncbi.nlm.nih.gov/pubmed/36433503 http://dx.doi.org/10.3390/s22228906 |
Ejemplares similares
-
Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings
por: Zhou, Hongdi, et al.
Publicado: (2017) -
Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis
por: Song, Xinmin, et al.
Publicado: (2023) -
Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network
por: Zhang, Xiong, et al.
Publicado: (2023) -
An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis
por: Xu, Meng, et al.
Publicado: (2023) -
Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
por: Tian, He, et al.
Publicado: (2023)