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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
X. Wang, F. Lu, M. C. Zhou, and Q. Zeng, “A new knowledge mining and root cause analysis methodology for multivariate time series,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1–14, May 2026. doi: 10.1109/JAS.2026.125837
Citation: X. Wang, F. Lu, M. C. Zhou, and Q. Zeng, “A new knowledge mining and root cause analysis methodology for multivariate time series,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1–14, May 2026. doi: 10.1109/JAS.2026.125837
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A New Knowledge Mining and Root Cause Analysis Methodology for Multivariate Time Series

doi: 10.1109/JAS.2026.125837
Funds:  This work was supported by the National Science and Technology Major Project of China (2022ZD0119501), the Science and Technology Development Fund of Shandong Province (ZR2022MF288, ZR2023MF097), and the Fundo para o Desenvolvimento das Ciências e da Tecnologia (0047/2021/A1)
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    Abstract
  • Root cause analysis (RCA) aims to discover the root causes of abnormal events. Causal relations reveal the evolution process of abnormal events, which plays a crucial role in RCA. However, existing temporal causal discovery methods neither explicitly emphasize the “AND/OR” relations among causes, nor consider the synergy effects owned by non-causal variables on causal rules, thereby affecting the credibility of RCA. To address the issues, by fusing Petri nets and Bayesian networks, this study proposes a new knowledge mining and RCA methodology for multivariate time series, called Synergy-incorporated Bayesian Time Petri Net. It integrates the advantages of Petri nets in modeling and analyzing complex temporal dependencies and Bayesian networks in evidence reasoning. It takes into account “AND/OR” relations and synergy effects in temporal knowledge mining and RCA. Two cases are employed to verify the performance of the proposed methodology in knowledge mining and RCA, including a case study of quality anomaly detection of solar panel and the Tennessee Eastman process. The experimental results from both cases indicate that the proposed methodology can effectively consider “AND/OR” relations and synergy effects. In particular, when applied to the diagnosis of quality issues in solar panels, the proposed methodology outperforms the state-of-the-art RCA methods in accuracy by over 11%.

     

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