Aiming at the large-scale complex industrial process, a fault diagnosis method based on multiblock kernel
principal component analysis(MBKPCA) and signed directed graph(SDG) is proposed. Firstly, by proposing a partition
strategy based on SDG and priority, the process is divided into multiple blocks according to the strong connected component
as the highest priority, the multiple input or output degree node group as the second priority and the node chain as the lowest
priority. On that basis, MBKPCA is used for the process monitoring. If the fault is detected, MBKPCA will determine which
block the fault occurs in, then SDG is triggered to complete the fault location in the fault block. The proposed method can
improve the accuracy and rapid of the fault diagnosis for the complex industrial process by overcoming the disadvantage of
the incomplete fault isolation of MBKPCA and combination explosion in SDG reasoning process. The simulation research
on Tennessee Eastman process is performed to show the effectiveness of the proposed method.