A new fault detection and diagnosis method based on principal component analysis in multivariate continuous processes

The fault detection and diagnosis methods based on principal component analysis (PCA) have been developed widely because they need no detailed information about process mechanism model and really can detect fault promptly. However the existed diagnosis algorithms such as expert system or contribution plot etc. still have some troubles when applied in real industrial processes, which leads to more extensive research on this topic. In this paper, the proposed diagnosis method utilizes on-line loading plot and cluster analysis to give accurate cause for abnormal process condition, which is grounded on the fact that faults normally change the correlation of process variables which may indicate more direct information about the failure cause. Thus, the principal components score plot and square predicted error (SPE) plot are used to detect process abnormal operation condition, the loading plot and cluster analysis are used to diagnose the faults. The method is also applied to monitor the fractional distillation process of liquefied gases. The result shows that accurate conclusion could be obtained easily by this method.