In the field of chemical process monitoring, the vine copula model provides a new idea for describing the interdependence between high-dimensional complex variables, and directly characterizes the correlation without dimensional reduction. However, in actual industrial processes, the number of pair copulas to be optimized and the parameters to be estimated increase rapidly when the dimensionality of the variables is large. This greatly increases the computational load and reduces the detection efficiency. In this paper, a fault diagnosis method based on a simplified R-vine (SRV) model is proposed. Without reducing the precision of the model significantly, the simplified level is set to reduce the complexity of the workload and calculations. The simplified level of an R-vine model is obtained by a Vuong test. Then, the generalized local probability (GLP) of the non-Gaussian state is constructed by using the theory of highest density region (HDR) and a density quantile table. The monitoring results of the Tennessee Eastman (TE) process and a real acetic acid dehydration distillation system show that the proposed SRV approach achieves good performance in monitoring results and computational load for chemical process fault monitoring.

中文翻译：

---

基于简化的R-Vine Copula的非线性非高斯过程监控

在化学过程监测领域，藤蔓菌落模型为描述高维复杂变量之间的相互依赖关系提供了新思路，并直接表征了相关性而没有降维。然而，在实际的工业过程中，当变量的维数较大时，要优化的配对对数和要估计的参数迅速增加。这极大地增加了计算负荷并降低了检测效率。提出了一种基于简化R-vine（SRV）模型的故障诊断方法。在不显着降低模型精度的情况下，设置了简化级别以减少工作量和计算的复杂性。R-vine模型的简化级别是通过Vuong测试获得的。然后，利用最高密度区域理论和密度分位数表，构造了非高斯状态的广义局部概率（GLP）。田纳西州伊士曼（TE）工艺和实际的乙酸脱水蒸馏系统的监测结果表明，所提出的SRV方法在监测结果和化学过程故障监测的计算量方面取得了良好的性能。