False alarm rates of fault detection methods

Abstract

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.

Keywords

• 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