Yusuf Sevim

Hata bulma yöntemlerinin yanlış alarm oranları

Bu çalışma bağımsız bileşen analiz (BBA) ve temel bileşen analiz (TBA) algoritmalarının Tennessee Eastman (TE) süreci üzerindeki hata bulma ve yanlış alarm oranları (YAO) üzerine yoğunlaşmaktadır. TBA ve ICA algoritmaları, veri tabanlı hata bulmak için oldukça fazla uygulanmalarına rağmen, algoritmaların YAO üzerine sınırlı çalışma vardır. Bu çalışmada, algoritmaların YAO’ları TE süreci üzerinde incelenecektir. Simülasyon çalışmaları, sunulan algoritmalar hata bulmada oldukça doğruyken, YAO’ları için BBA’nın TBA’dan daha yüksek performansa sahip olduğunu göstermiştir.

Anahtar Kelimeler:

Süreç Gözlemleme, Hata Bulma, Bağımsız Bileşen Analiz, Temel Bileşen Analiz, T^2 istatistik

False alarm rates of fault detection methods

This study focuses on the fault detection (FD) and false alarm rates (FAR) of Principal component analysis (PCA) and independent component analysis (ICA) algorithms on the Tennessee Eastman (TE) process. However, PCA and ICA algorithms have been applied widely to systems for data driven fault detection, there are limited work on FARs of the algorithms. In this work, FARs of the algorithms are investigated on TE process. Simulation study indicates that the proposed algorithms are robust for fault detection, and ICA has higher performance than PCA for FARs.

Keywords:

Process Monitoring, Fault Detection, Independent Component Analysis, Principal Component Analysis, T^2 Statistics,

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