An integrated faults classification approach based on LW-MWPCA and PNN

This paper presents the development of an algorithm based on lifting wavelets, moving window principal components analysis and probabilistic neural network (LW-MWPCA and PNN) for classifying the industrial system faults. The proposed technique consists of a pre-processing unit based on lifting wavelets transform in combination with moving window principal components analysis (MWPCA) and PNN. Firstly the data are pre-processed to remove noise through lifting scheme wavelets, which are faster than first generation wavelets, MWPCA is used to reduce the dimensionality, and then PNN is used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, the method based on LW-MPCA and PNN is applied to diagnose the faults in TE Process. Simulation studies show that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions, but also is reliable, fast and computationally efficient tool.