This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by integrating the principal component analysis (PCA) with the Bayesian network (BN). Though the integration of PCA-BN for FDD purposes has been studied in the past, the present work makes two contributions for process systems. First, the application of correlation dimension (CD) to select principal components (PCs) automatically. Second, the use of Kullback-Leibler divergence (KLD) and copula theory to develop a data-based BN learning technique. The proposed method uses a combination of vine copula and Bayes’ theorem (BT) to capture nonlinear dependence of high-dimensional process data which eliminates the need for discretization of continuous data. The data-driven integrated PCA-BN framework has been applied to two processing systems. Performance of the proposed methodology is compared with the independent component analysis (ICA), kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and their integrated frameworks with the BN. The comparative study suggests that the proposed framework provides superior performance.

本文通过将主成分分析（PCA）与贝叶斯网络（BN）集成在一起，提出了一种数据驱动的故障检测与诊断方法（FDD）。尽管过去已经研究了用于FDD的PCA-BN集成，但目前的工作为过程系统做出了两个贡献。首先，应用相关维度（CD）自动选择主成分（PC）。第二，利用Kullback-Leibler散度（KLD）和copula理论来开发基于数据的BN学习技术。拟议的方法结合藤蔓copula和贝叶斯定理（BT）来捕获高维过程数据的非线性依赖性，从而消除了连续数据离散化的需要。数据驱动的集成PCA-BN框架已应用于两个处理系统。将所提出方法的性能与独立成分分析（ICA），内核主成分分析（KPCA），内核独立成分分析（KICA）以及它们与BN的集成框架进行了比较。对比研究表明，提出的框架可提供出色的性能。