Feature extraction in an ensemble of multiple local classifiers for fault diagnosis in industrial processes

被引：3
|
作者：
Santi, Gustavo B. [1 ]
Assis, Arthur A. [1 ]
Rebli, Victor N. [2 ]
Ciarelli, Patrick M. [1 ]
Rauber, Thomas W. [2 ]
Munaro, Celso J. [1 ]
机构：
[1] Univ Fed Espirito Santo, Lab Controle & Instrumentacao, Dept Engn Eletr, BR-29075910 Vitoria, ES, Brazil
[2] Univ Fed Espirito Santo, Nucleo Inferencia & Algoritmos, Dept Informat, BR-29075910 Vitoria, ES, Brazil
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 01期
基金：
美国国家科学基金会;
关键词：
Fault detection and diagnosis; feature extraction; classifier ensemble; Tennessee Eastman; RECONSTRUCTION-BASED CONTRIBUTION;
D O I：
10.1016/j.ifacol.2019.06.077
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work proposes a classification model for fault diagnosis. In a first stage, an unsupervised clustering algorithm discovers groups of potential fault classes. Subsequently, a specialized classifier is trained for each of the clusters, thus diminishing complexity and augmenting performance. The quality of the diagnosis is further improved by combining the classifiers in an ensemble. As a benchmark, data provided by the Tennessee Eastman chemical plant simulator was used, with promising results. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：293 / 298
页数：6
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