%A WANG Mengyuan, ZHANG Xiong, MA Liang, PENG Kaixiang %T Fault diagnosis for industrial processes based on causal topological graph %0 Journal Article %D 2017 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2017.239 %P 187-194 %V 47 %N 5 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1661.shtml} %8 2017-10-20 %X With the combination of the process knowledge and data driven methods, the fault diagnosis method based on causal topological graph could effectively deal with the fault location and fault propagation identification. A correlation index(CI)based on partial correlation coefficient was applied to the causal topological graph to analyze the correlation between variables quantitatively. The proposed CI was monitored via probabilistic principal component analysis method(PPCA)for fault detection. The concept of mean weighted value and causal topological graph were introduced in order to identify the optimal fault propagation path based on reconstruction-based contribution(RBC)after detecting a fault. The effectiveness of the method was verified by the application of hot strip mill process(HSMP). The results showed that the proposed method could effectively identify the fault roots and propagation paths.