JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 187-194.doi: 10.6040/j.issn.1672-3961.0.2017.239

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Fault diagnosis for industrial processes based on causal topological graph

WANG Mengyuan1,2, ZHANG Xiong1.2, MA Liang1,2, PENG Kaixiang1,2   

  1. 1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

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Abstract: 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.

Key words: PPCA, correlation analysis, fault diagnosis, causal topological graph, RBC

CLC Number: 

  • TP237
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