Causal Discovery in Dynamic Industrial Systems Under Parametric Uncertainty: A Polynomial Chaos Approach

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Abstract
Industrial processes are governed by causal relationships whose understanding is essential for root cause analysis and predictive maintenance. However, existing causal discovery methods operate in batch mode and assume static causal structures, conflicting with the inherent nonstationarity caused by equipment degradation and regime changes. Current approaches cannot track how causal effects vary with operating parameters in real-time, nor detect structural change points. This article introduces Online-PCT-CD, a framework that extends parametric causal discovery to streaming data settings. The proposed method integrates incremental Polynomial Chaos Expansion estimation via recursive least squares, dual-layer cumulative sum change point detection, and an adaptive sliding window strategy. Experiments on an industrial chemical process benchmark demonstrate that Online-PCT-CD achieves 93.7% average F1-score, outperforming 17 baseline methods while maintaining 450 samples per second throughput suitable for real-time deployment.
Funding Information
  • National Natural Science Foundation of China (62373361)

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