Self-adaptive signal processing integrated wavelet theory & PCA for process monitoring

A new method of wavelet theory-based self-adaptive multi-scale principal component analysis (W-AMSPCA) is proposed for process signal monitoring and diagnosis. The technique uses the wavelet analysis to decompose the signals and reconstruct the signals in order to denoise with disturbance and outliers, and then uses the adaptive PCA algorithm 10 reduce the dimensions of process signals and identify different wavelet coefficients based on the multi-scale decomposition. The proposed method can early find and analyze the slow and feeble changes of process signals that can´t be monitored by normal PCA. Furthermore, the performing framework and algorithm of W-AMSPCA for on-line signal monitoring and diagnosis are proposed.