Interval-valued PCA-based approach for fault detection in dynamic systems

Abstract

Fault detection and diagnosis is an important domain in modern process engineering, where principal component analysis (PCA) is one of its powerful data-driven techniques. The use of PCA in dynamic systems will approximate the dynamic behavior with a static one, which is not convenient. To address this issue, one of the most well-known approaches is the use of time- lag-shifted data; this approach is known as dynamic principal component analysis (DPCA). However, DPCA is still not an optimal solution due to the effect of uncertainties on the model parameters, which will lead to drifts and affect the performance of the model. In this disser- tation, a new approach is proposed to overcome this issue by including uncertainties in the modeling phase, which will ensure a safe interval for the data to fluctuate. This approach is called interval-valued dynamic principal component analysis (IV-DPCA). To test the perfor- mance of IV-DPCA, real data obtained from a cement manufacturing plant were used to build and test the PCA, DPCA, and IV-DPCA models, then the three models were compared to each other in terms of false alarm rate (FAR), missed alarms rate (MDR), and detection time delay (DTD).