Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference

被引：0

作者：

Sanchez-Fernandez, A. ^{[1
]}

Fuente, M. J. ^{[1
]}

Sainz-Palmero, G. I. ^{[1
]}

机构：

[1] Univ Valladolid, EII, Dept Syst Engn & Automat Control, Valladolid, Spain

来源：

2018 IEEE 23RD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | 2018年

关键词：

Fault detection; Dynamic principal component analysis; Decentralized process monitoring; Decision fusion; REGRESSION; DIAGNOSIS; SELECTION; PLS;

D O I：

暂无

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 paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness.

引用

页码：800 / 807

页数：8

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