Fault monitoring of TE based on improved multiscale principal component analysis

In order to handle the problem of nonstationary and random nature of data in the process industry, an improved multiscale principal component analysis is proposed, which contains different noises inevitably. Firstly, an improved wavelet threshold denoising method which combines multiple wavelet transform with a new threshold function based on the characteristics of wavelet analysis is proposed. The data collected from the industry condition are processed by means of the improved wavelet threshold denoising method. Using wavelets, the individual variable is decomposed into approximations and details at different scales. Contributions from each scale are collected in separate matrices, and a PCA model is then constructed to extract correlation at each scale. According to the simulation of Tennessee Eastman, and comparing the improved MSPCA with traditional PCA, it shows that the improved MSPCA has enhanced the accuracy of process monitoring.