Abstract: Principal component analysis (PCA) finds wide application in chemical process monitoring and product quality control as a data-driven modeling method.Based on the concept on fault subspace, the fault reconstruction issue was explored by using T² index, while the geometric method recently developed by Dunia et al focuses on the SPE index.However, some faults involving process fault and sensor fault that do not violate the PCA statistical model can only be detected by T² index.Thus the proposed reconstruction approach has superior performance in the general sense.The acquired results were then illustrated and verified by monitoring a simulated double-effect evaporator (DEE) process, where different sensor faults were reconstructed and fault wave/magnitude was estimated to judge the sensor fault type.