Fault detection approach based on principal component analysis (PCA) may perform not well when the process
is time-varying, because it can cause unfavorable influence on feature extraction. To solve this problem, a modified PCA
which considering variance maximization is proposed, referred to as weighted PCA (WPCA). WPCA can obtain the slow
features information of observed data in time-varying system. The monitoring statistical indices are based on WPCA
model and their confidence limits are computed by kernel density estimation (KDE). A simulation example on continuous
stirred tank reactor (CSTR) show that the proposed method achieves better performance from the perspective of both fault
detection rate and fault detection time than conventional PCA model.