Residual generation is the essental step in the model-based fault detection methods, but it has not been applied in the statistical process monitoring approaches. Therefore, a missing data based residual generation strategy is proposed, the generated residual which can indicate the fittness of sampled data to the developed statistical model is utilzied for fault detection. The proposed missing data based principal component analsyis (MD-PCA) method first assumes the measured data of individual variables is missing one by one, and the technique handling missing data is then employed for calculating the estimation of the corresponding missig variable. Ultimately, the resdiual between the actual and estimated data is modeled and monitored using the PCA-based fault detection approach. The advantages of utilizing residual for fault detection lie in that the generated residual can reduce the non-Gaussianity of the origianl measured variable to some extent, and that the residual reflects the uncorrelated information from other measured variables in the corresponding missing variable, and more essential characteristic of indicidual variables can be recovered. The case study in the TE process sufficiently demonstrates these advantages of the proposed method, and the feasibility and superiority of the MD-PCA method are validated as well.