New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln

Abstract

Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in industrial process monitoring. It intends to promptly detect and identify abnormalities and enhance the reliability and safety of the processes. Kernel Principal Component Analysis (KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its ability in monitoring nonlinear systems. However, KPCA suffers from high computing time and large storage space when a large-sized training dataset is used. So, extracting and selecting the more relevant observations could provide a good solution to high computation time and memory requirements costs. In this paper, a new Reduced KPCA (RKPCA) approach is developed to address that issue. It aims to preserve one representative observation for each similar and selected Euclidean distance between training samples. Afterwards, the obtained reduced training dataset is used to build a KPCA model for FDD purposes. The developed RKPCA scheme is tested and evaluated across a numerical example and an actual involuntary process fault and various simulated sensor faults in a cement plant. The obtained results show high monitoring performance with highest robustness to false alarms and maximum fault detection sensitivity compared to conventional PCA, KPCA and other well-established RKPCA techniques. Furthermore, the unified contribution plot method demonstrates superior potentials in identifying faulty variables.