Abstract: In industrial processes, measured data are often contaminated by noise, which causes poor performance of some techniques driven by data. Wavelet transform is a useful tool to de-noise the process information, but conventional transaction is directly employing wavelet transform to the measured variables, which will make the method less effective and more multifarious if there exists lots of process variables and collinear relationships. In this paper, a novel multivariate statistical projection analysis (MSPA) based on data de-noised with wavelet transform and blind signal analysis is presented, which can detect fault more quickly and improve the monitoring performance of the process. The simulation results applying to a double-effect evaporator verify higher effectiveness and better performance of the new MSPA than classical multivariate statistical process control(MSPC).