Considering the weaknesses of traditional principal component analysis (PCA) in dealing with nonlinear correlations and non-Gaussian distribution data, PCA is optimized by replacing covariance matrix with Spearman ranking correlation coefficient (SRCC) matrix and introducing Gaussian transition by Johnson transformation. Because the commonly used BN that simply identifies a node as faulty or normal states sometimes fails to diagnose critical operation information, multi-state Bayesian network (MBN) is developed to recognize a node into multiple states. To fulfill process monitoring task, the optimized PCA (OPCA) and MBN integrated method (OPCA-MBN) is proposed in this paper. OPCA is utilized to detect faults and provide evidence to MBN for diagnosing fault or normal oscillation propagation pathways. In the modeling process of MBN, the causal relationships between tangled internal variables are determined using Transfer entropy and process knowledge. The practicability and effectiveness of the proposed method are demonstrated through the application in the Tennessee Eastman (TE) process in comparison with two-state BN.

考虑到传统主成分分析(PCA)在处理非线性相关和非高斯分布数据方面的弱点，将协方差矩阵替换为Spearman等级相关系数(SRCC)矩阵，并通过Johnson变换引入高斯转移，对PCA进行了优化。由于常用的简单地将节点识别为故障或正常状态的 BN 有时无法诊断关键操作信息，因此开发了多状态贝叶斯网络 (MBN) 以将节点识别为多个状态。为了完成过程监控任务，本文提出了优化的PCA（OPCA）和MBN集成方法（OPCA-MBN）。OPCA 用于检测故障并为 MBN 诊断故障或正常振荡传播路径提供证据。在MBN的建模过程中，纠结的内部变量之间的因果关系是使用传递熵和过程知识确定的。通过在田纳西伊士曼 (TE) 工艺中的应用与两态 BN 进行比较，证明了所提出方法的实用性和有效性。