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 the process mechanism model and really can detect faults promptly. However the existing diagnosis algorithms such as expert systems or contribution plots, etc. still have some trouble when they are applied in real industrial processes, which leads to more extensive research on this topic. In this paper, the proposed diagnosis method utilizes the on-line loading plot and cluster analysis to give accurate cause for abnormal process conditions, 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 the abnormal process operation condition, the loading plot and cluster analysis are used to diagnose the faults. The result shows that accurate conclusion could be obtained easily by this method.