Wang Jing, Liu Pengyang, Lu Shan, Zhou Meng, Chen Xiaolu
School of Electrical and Control Engineering, North China University of Technology, Beijing 100043, China.
Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
ISA Trans. 2023 Feb;133:42-52. doi: 10.1016/j.isatra.2022.07.017. Epub 2022 Jul 20.
A decentralized fault detection and diagnosis method is proposed to monitor the nonlinear plant-wide processes effectively. It includes two theme activities: mutual information-Louvain based process decomposition and support vector data descriptions (SVDD) based fault diagnosis. Firstly, the plant-wide process is preliminarily map as an undirected graph corresponding to the mechanism knowledge and process structure. Mutual information (MI) is introduced to depict the correlation degree between different nodes (i.e., process variables), and a Louvain algorithm with MI correlation is proposed to fine decompose the process into reasonable sub-blocks. Then, decentralized SVDD based fault detection method is presented for each sub-block, and the corresponding variable contribution rate is derived. Finally, a Bayesian fusion inference is given to evaluate the detection results of all sub-blocks in an integrated manner. The proposed method is verified in the Tennessee-Eastman (TE) process.
提出了一种分散式故障检测与诊断方法，以有效监测非线性全流程工厂过程。它包括两个主题活动：基于互信息 - 鲁汶算法的过程分解和基于支持向量数据描述（SVDD）的故障诊断。首先，根据机理知识和过程结构将全流程工厂过程初步映射为一个无向图。引入互信息（MI）来描述不同节点（即过程变量）之间的相关程度，并提出一种具有MI相关性的鲁汶算法，将过程精细分解为合理的子块。然后，针对每个子块提出基于分散式SVDD的故障检测方法，并推导相应的变量贡献率。最后，进行贝叶斯融合推理，以综合方式评估所有子块的检测结果。该方法在田纳西 - 伊斯曼（TE）过程中得到了验证。
