Cargando…
Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples
Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill bearing by employing the imp...
Autores principales: | Peng, Rongrong, Zhang, Xingzhong, Shi, Peiming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610296/ https://www.ncbi.nlm.nih.gov/pubmed/36298167 http://dx.doi.org/10.3390/s22207815 |
Ejemplares similares
-
Multi-Representation Domain Adaptation Network with Duplex Adversarial Learning for Hot-Rolling Mill Fault Diagnosis
por: Peng, Rongrong, et al.
Publicado: (2022) -
Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet †
por: Mohiuddin, Mohammad, et al.
Publicado: (2023) -
Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy
por: Yan, Xiaoan, et al.
Publicado: (2021) -
Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet
por: He, Kecheng, et al.
Publicado: (2023) -
The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
por: Zhuang, Deyu, et al.
Publicado: (2023)