Fault Classification in Dynamic Processes Using Multiclass Relevance Vector Machine and Slow Feature Analysis

Fault Classification in Dynamic Processes Using Multiclass Relevance Vector Machine and Slow Feature Analysis

This paper proposes a modified relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learnin...

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Bibliographic Details
Main Authors: Jian Huang, Xu Yang, Yuri A. W. Shardt, Xuefeng Yan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Slow feature analysis
relevance vector machine
dynamic process
fault classification
process monitoring
statistical learning
Online Access:https://ieeexplore.ieee.org/document/8941098/

Internet

https://ieeexplore.ieee.org/document/8941098/

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