Cargando…
Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines
With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rap...
Autores principales: | Shan, Nanliang, Xu, Xinghua, Bao, Xianqiang, Qiu, Shaohua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182897/ https://www.ncbi.nlm.nih.gov/pubmed/35684620 http://dx.doi.org/10.3390/s22113997 |
Ejemplares similares
-
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
por: Qiu, Shaohua, et al.
Publicado: (2023) -
Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis
por: Rodriguez, Nibaldo, et al.
Publicado: (2019) -
A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods
por: Li, Zhenbao, et al.
Publicado: (2021) -
Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model
por: Lu, Lixin, et al.
Publicado: (2023) -
Alumina Concentration Detection Based on the Kernel Extreme Learning Machine
por: Zhang, Sen, et al.
Publicado: (2017)