saujs Sakarya University Journal of Science 2147-835X Sakarya University 10.16984/saufenbilder.310240 Electrical Engineering Elektrik Mühendisliği False alarm rates of fault detection methods Hata bulma yöntemlerinin yanlış alarm oranları Sevim Yusuf 02 01 2018 22 1 49 55 05 03 2017 08 29 2017 Copyright © 1997, Sakarya University Journal of Science 1997 Sakarya University Journal of Science

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.

Buçalışma bağımsız bileşen analiz (BBA) ve temel bileşen analiz (TBA)algoritmalarının Tennessee Eastman (TE) süreci üzerindeki hata bulma ve yanlışalarm oranları (YAO) üzerine yoğunlaşmaktadır. TBA ve ICA algoritmaları, veritabanlı hata bulmak için oldukça fazla uygulanmalarına rağmen, algoritmalarınYAO üzerine sınırlı çalışma vardır. Bu çalışmada, algoritmaların YAO’ları TEsüreci üzerinde incelenecektir. Simülasyon çalışmaları, sunulan algoritmalarhata bulmada oldukça doğruyken, YAO’ları için BBA’nın TBA’dan daha yüksekperformansa sahip olduğunu göstermiştir.

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