Cargando…
A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field...
Autores principales: | Gong, Wenfeng, Chen, Hui, Zhang, Zehui, Zhang, Meiling, Wang, Ruihan, Guan, Cong, Wang, Qin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479910/ https://www.ncbi.nlm.nih.gov/pubmed/30970672 http://dx.doi.org/10.3390/s19071693 |
Ejemplares similares
-
Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis
por: Xiao, Qiyang, et al.
Publicado: (2022) -
An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery
por: Zhang, Long, et al.
Publicado: (2022) -
Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer
por: Ye, Lihao, et al.
Publicado: (2021) -
Intelligent fault diagnosis and remaining useful life prediction of rotating machinery
por: Lei, Yaguo
Publicado: (2016) -
A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm
por: Fu, Wenlong, et al.
Publicado: (2018)