Cargando…
Rolling Bearing Fault Diagnosis Using Multi-Sensor Data Fusion Based on 1D-CNN Model
To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is proposed in this paper. Firstly, the BAS optimal algorithm is adopted to obtain the optimal parameters of SWD. After that...
Autores principales: | Wang, Hongwei, Sun, Wenlei, He, Li, Zhou, Jianxing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141983/ https://www.ncbi.nlm.nih.gov/pubmed/35626458 http://dx.doi.org/10.3390/e24050573 |
Ejemplares similares
-
On the Accuracy of Fault Diagnosis for Rolling Element Bearings Using Improved DFA and Multi-Sensor Data Fusion Method
por: Song, Qiang, et al.
Publicado: (2020) -
Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
por: Tian, He, et al.
Publicado: (2023) -
A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN
por: Wang, Mengjiao, et al.
Publicado: (2022) -
Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings
por: Lv, Jie, et al.
Publicado: (2021) -
WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing
por: Liu, Ruozhu, et al.
Publicado: (2023)