Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm

Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm

To solve the problem caused by kernel entropy component analysis (KECA) for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis (EKECA) was proposed.Firstly,a series of kernel functions with different w...

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Autores principales: Jinyu GUO, Wenjun ZHAO, Yuan LI
Formato: article
Lenguaje:ZH
Publicado: Hebei University of Science and Technology 2021
Materias:
other disciplines of automatic control technology; kernel entropy component analysis; gaussian kernel function; bayesian decision; ensemble learning method
Technology
T
Acceso en línea:https://doaj.org/article/5d81e46cb4ae45be968e1cc56ba0008d
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