False alarm rates of fault detection methods

False alarm rates of fault detection methods

This study focuses on the fault detection (FD) andfalse alarm rates (FAR) of Principal component analysis (PCA) and independent component analysis (ICA)algorithms on the Tennessee Eastman (TE) process. However, PCA and ICAalgorithms have been applied widely to systems for data driven faultdetection,...

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Bibliographic Details
Main Author: Yusuf Sevim
Format: Article
Language:English
Published: Sakarya University 2018-02-01
Series:Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Subjects:
process monitoring
fault detection
independent component analysis
principal component analysis
t^2 statistics
süreç gözlemleme
hata bulma
bağımsız bileşen analiz
temel bileşen analiz
t^2 istatistik
Online Access:https://dergipark.org.tr/tr/download/article-file/340872
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Summary:This study focuses on the fault detection (FD) andfalse alarm rates (FAR) of Principal component analysis (PCA) and independent component analysis (ICA)algorithms on the Tennessee Eastman (TE) process. However, PCA and ICAalgorithms have been applied widely to systems for data driven faultdetection, there are limited work on FARs of the algorithms. In this work, FARs of the algorithms areinvestigated on TE process. Simulation study indicates that the proposedalgorithms are robust for fault detection, and ICA has higher performance thanPCA for FARs.
ISSN:2147-835X

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