Process monitoring method based on improved dynamic multi-scale principal component analysis

A process monitoring approach based on improved multi-scale dynamic principal component analysis (IMSDPCA) is proposed to handle the multi-scale and dynamic characteristics of industrial process data. Augmented data matrix is structured by using the "time lag shift" method, and then multi-scale measurement characteristics of the wavelet is used to analyze the augmented matrix of the measuring variable in multiplicity scale. So the problem that the measuring variables have the dynamic and the multi-scale characteristics of industrial process data is not only resolved, but the shortcomings that the number of principal components is too much is overcome, on the basis of this, the monitoring index based on T² and SPE is improved using. The simulation results of the Tennessee Eastman chemical process faults based on MSDPCA monitoring method shows that the IMSDPCA algorithm is feasible and effective, the process monitoring performance is improved compare with PCA and DPCA approach.