This paper presents a risk-based fault detection and diagnosis methodology for nonlinear and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine model provides a multivariate probability that is used in the event tree to generate a dynamic risk profile. An abnormal situation is detected from the monitored risk profile; subsequently, root cause(s) diagnosis is carried out. A fault diagnosis module is also proposed using the density quantiles, developed from marginal probabilities. The performance of this methodology is benchmarked using the Tennessee Eastman chemical process. The proposed risk-based framework has also been applied to an experimental setup and a real industrial isomer separator unit. The diagnosis module is found sensitive to both single and simultaneous faults. The results confirm that the proposed methodology provides better performance than the conventional principal component analysis and transfer entropy-based fault diagnosis techniques using the advantage of marginal density quantile analysis.

本文提出了一种使用R-vine copula和事件树的非线性和非高斯过程系统基于风险的故障检测和诊断方法。R-vine模型提供了在事件树中用于生成动态风险概况的多元概率。从监视的风险概况中检测到异常情况；随后，进行根本原因诊断。还提出了一种利用边缘概率发展的密度分位数的故障诊断模块。该方法的性能使用田纳西州伊士曼化学工艺进行了基准测试。所提出的基于风险的框架也已应用于实验装置和实际的工业异构体分离器装置。发现诊断模块对单个故障和同时故障都很敏感。