Cargando…
A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm
The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-d...
Autores principales: | Zhou, Xiangyu, Mao, Shanjun, Li, Mei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401700/ https://www.ncbi.nlm.nih.gov/pubmed/34450970 http://dx.doi.org/10.3390/s21165532 |
Ejemplares similares
-
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) -
Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings
por: Lv, Jie, et al.
Publicado: (2021) -
Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis
por: Li, Xudong, et al.
Publicado: (2021) -
Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain
por: Hakim, Mohammed, et al.
Publicado: (2022)