Fault Detection and Diagnosis on Process Control Systems Using Ensemble Learning Algorithms from Poincare Plot Measures

Abstract

This study aimed to detect and classify 20 different malfunctions in an industrial facility that involves nonlinear processes from various chemical units. The IEEEDataPort online dataset, acquired from a large industrial plant, was used in this study. It contains measures from 52 process points in Tennessee Eastman Process with 20 different fault types. We extracted two commonly used nonlinear features from Poincare Plots for each measurement point. The statistically meaningful features, which show statistically significant differences among fault types with a significance of 5%, were selected from these features. Five distinct Ensemble Learner algorithms (Boosted Trees, Bagged Trees, Subspace Discriminant, Subspace KNN, and RUSBoosted Trees) discriminated the fault types using all features and the selected features only. The maximum classifier accuracies were 89.5% for both feature sets using the Subspace Discriminant method in this study. This performance is a comprehendible result among the results achieved in similar studies. On the other hand, ANOVA-based feature selection didn't result in a clear advantage to diagnose faults in such industrial process plants.

Keywords

• Tennessee Eastman Process System
• Fault Detection
• Fault Diagnosis
• Ensemble Learning
• Poincare Plot Measures
• One Way ANOVA Test.

Poincare Çizimi Ölçümlerinden Topluluk Öğrenmesi Yöntemleri Kullanılarak Proses Kontrol Sistemlerinde Arıza Tespit ve Teşhisi

Öz

Bu çalışmada, farklı kimyasal birimlere ait doğrusal olmayan süreçler içeren bir endüstriyel tesisteki 20 farklı arızanın tespiti ve sınıflandırılması yapılmıştır. Kullanılan veri seti büyük bir endüstriyel tesisten elde edilen IEEEDataPort çevrimiçi veri kümesidir. Tennessee Eastman Süreci olarak bilinen bu veri seti 20 farklı hata türü ile 52 işlem noktasından alınan ölçümleri içerir. Bu ölçümler üzerinden Poincare çizimleri elde edilerek her işlem noktası için sık kullanılan doğrusal olmayan öznitelikler çıkarılmıştır. Bu öznitelikler %5 istatistiksel anlamlılık düzeyinde tek yönlü ANOVA testine uygulanarak hata türleri arasında istatistiksel olarak anlamlı fark olduğunu gösterenler seçilmiştir. Hem tüm öznitelikler hem de sadece ANOVA ile seçilen öznitelikler beş farklı topluluk öğrenmesi algoritması (Boosted Trees, Bagged Trees, Subspace Discriminant, Subspace KNN ve RUSBoosted Trees) kullanılarak sınıflandırılmıştır. Bu çalışmada elde edilen en yüksek sınıflandırıcı doğruluğu Subspace Discriminant algor itması kulanılarak %89,5 olarak elde edilmiştir. Aynı verisetini kullanan benzer çalışmalarla kıyaslanabilir bir başarı düzeyine ulaşılmıştır. Öte yandan, ANOVA tabanlı öznitelik seçiminin bu tür endüstriyel proses tesislerinde arızaların teşhisinde bariz bir üstünlük sağlamadığı görülmüştür.

Anahtar Kelimeler

• Tennessee Eastman Proses Sistemi
• Arıza Tespiti
• Arıza Teşhisi
• Topluluk Öğrenmesi
• Poincare Grafik Ölçümleri
• Tek Yönlü ANOVA Testi.

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