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Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining

Author

Listed:
  • Karim Nadim

    (Polytechnique Montréal
    CanmetENERGY – Natural Resources Canada (NRCan))

  • Ahmed Ragab

    (Polytechnique Montréal
    CanmetENERGY – Natural Resources Canada (NRCan)
    Menoufia University)

  • Mohamed-Salah Ouali

    (Polytechnique Montréal)

Abstract

The complexity of industrial processes imposes a lot of challenges in building accurate and representative causal models for abnormal events diagnosis, control and maintenance of equipment and process units. This paper presents an innovative data-driven causality modeling approach using interpretable machine learning and process mining techniques, in addition to human expertise, to efficiently and automatically capture the complex dynamics of industrial systems. The approach tackles a significant challenge in the causality analysis community, which is the discovery of high-level causal models from low-level continuous observations. It is based on the exploitation of event data logs by analyzing the dependency relationships between events to generate accurate multi-level models that can take the form of various state-event diagrams. Highly accurate and trustworthy patterns are extracted from the original data using interpretable machine learning integrated with a model enhancement technique to construct event data logs. Afterward, the causal model is generated from the event log using the inductive miner technique, which is one of the most powerful process mining techniques. The causal model generated is a Petri net model, which is used to infer causality between important events as well as a visualization tool for real-time tracking of the system’s dynamics. The proposed causality modeling approach has been successfully tested based on a real industrial dataset acquired from complex equipment in a Kraft pulp mill located in eastern Canada. The generated causality model was validated by ensuring high model fitness scores, in addition to the process expert’s validation of the results.

Suggested Citation

  • Karim Nadim & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 57-83, January.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-021-01903-y
    DOI: 10.1007/s10845-021-01903-y
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    References listed on IDEAS

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    1. Ahmed Ragab & Soumaya Yacout & Mohamed-Salah Ouali & Hany Osman, 2019. "Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 255-274, January.
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    3. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    4. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
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