Online approach of fault diagnosis based on Lifting Wavelets and Moving Window PCA

Abstract

To online monitor process, a combined approach of fault detection and diagnosis based on Lifting Wavelets and Moving Window PCA (LW-MWPCA) was presented. Firstly the data were pre-processed to remove noise and spikes through lifting scheme wavelets, and then MWPCA was used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, LW-MWPCA was applied to diagnose the faults in TE Process. The results were given to show the effectiveness of these improvements for fault diagnosis performance in terms of low computational cost and high fault diagnosis rate.