Fault diagnosis is critical for ensuring safe and stable chemical production. Correct identification of causal relationships among variables in large-scale chemical processes is a prerequisite for analyzing the root causes and propagation paths of faults. However, chemical process big data often exhibit nonlinearity and nonstationarity, and contain various forms of noise, rendering conventional causal discovery methods vulnerable. In this paper, a novel causal discovery method based on the causality-gated time series Transformer (CGTST) is proposed to address this challenge. By performing time series prediction using the Transformer-based model on the target variable, CGTST measures the causal strength by assessing the contribution of each variable to the prediction through the causality gate structure. Furthermore, a causal validation method based on permutation feature importance is proposed to eliminate spurious causal relationships and ensure robust results. To enhance the performance of causal discovery on nonlinear and nonstationary chemical process data, ensemble empirical mode decomposition is employed to reduce noise. The CGTST-based method is validated on three case studies: a continuous stirred-tank reactor, the Tennessee Eastman process, and a real-world continuous catalytic reforming process. Our findings demonstrate that the proposed method outperforms conventional causal discovery methods and holds promising prospects for industrial applications.

故障诊断对于确保安全稳定的化工生产至关重要。正确识别大型化工过程中变量之间的因果关系是分析故障根源和传播路径的前提。然而，化工过程大数据往往表现出非线性和非平稳性，并包含各种形式的噪声，使传统的因果发现方法变得脆弱。在本文中，提出了一种基于因果关系门控时间序列转换器 (CGTST) 的新型因果发现方法来应对这一挑战。通过使用基于 Transformer 的模型对目标变量执行时间序列预测，CGTST 通过因果关系门结构评估每个变量对预测的贡献来衡量因果强度。此外，提出了一种基于排列特征重要性的因果验证方法，以消除虚假因果关系并确保稳健的结果。为了提高对非线性和非平稳化学过程数据进行因果发现的性能，采用集合经验模态分解来降低噪声。基于 CGTST 的方法在三个案例研究中得到验证：连续搅拌釜反应器、田纳西伊士曼工艺和真实世界的连续催化重整工艺。我们的研究结果表明，所提出的方法优于传统的因果发现方法，并具有工业应用前景。为了提高对非线性和非平稳化学过程数据进行因果发现的性能，采用集合经验模态分解来降低噪声。基于 CGTST 的方法在三个案例研究中得到验证：连续搅拌釜反应器、田纳西伊士曼工艺和真实世界的连续催化重整工艺。我们的研究结果表明，所提出的方法优于传统的因果发现方法，并具有工业应用前景。为了提高对非线性和非平稳化学过程数据进行因果发现的性能，采用集合经验模态分解来降低噪声。基于 CGTST 的方法在三个案例研究中得到验证：连续搅拌釜反应器、田纳西伊士曼工艺和真实世界的连续催化重整工艺。我们的研究结果表明，所提出的方法优于传统的因果发现方法，并具有良好的工业应用前景。