Multiblock strategy is widely used in plant-wide process monitoring to solve problems with complicated relationships between process variables. Traditional multiblock strategies and sub-block modeling methods are not effective in plant-wide process monitoring

because dynamic characteristics of the process have not been considered and knowledge or data information of the process is exclusively exploited. A mixed multiblock DMICA-PCA method was proposed to improve process monitoring performance. First

variables were sliced into initial sub-blocks by obtained process knowledge after analysis of process dynamics and further sliced into final sub-blocks by modified general Dice's coefficient (MGDC) between variables of initial sub-blocks. Then

the DMICA-PCA method was used to establish model and acquire statistical values of variables in final sub-blocks and a combined overall index from weighted sum was developed for fault detection

which improved performances by simultaneous diagnosis on each sub-block. Effectiveness of the proposed method was validated on monitoring the Tennessee-Eastman (TE) process.