A wavelet-based adaptive MSPCA for process signal monitoring & diagnosis

Abstract

A novelty method of wavelet-based adaptive multiscale principal component analysis (MSPCA) is proposed for process signal acquisition and diagnosis. The wavelet transform is used to decompose the process signals and at the same time analyze the different scales signals based on multiresolution signal analysis, and then the signals are reconstructed in order to denoise and get rid of disturbances. The adaptive PCA algorithm is adopted to monitor and diagnose abnormal situations on the basis of the multiscale wavelet coefficients, analyze the slow and feeble changes of fault signals that can't be acquisition and monitored by conventional PCA. Furthermore, the theoretic framework and practical process of wavelet-based adaptive MSPCA algorithm about online process signals monitoring and diagnosis are also proposed. Experimental simulations and practical application results verify the validity and dependability of the proposed method.