化工学报2018，Vol.69Issue(3)：1173-1181,9.DOI:10.11949/j.issn.0438-1157.20171104
基于多层优化PCC-SDG方法的化工过程故障诊断
A novel fault diagnosis method based on multilayer optimized PCC-SDG
摘要
Abstract
Chemical process failures are often caused by a series of variables with a chain effect. This study utilizes variable correlation characteristics, PCC (Pearson correlation coefficient) statistical index, and SDG (signed directed graph) to describe the causal relationship among variables, and then proposes a PCC-SDG fault diagnosis method based on a multi-layer optimization structure. With the topological network structure of the whole process as reference, this method first performs an initial optimization on the selected variable. An optimal PCC-SDG network is then constructed on the specific variables which have large PCA (principal component analysis) weights in the multilayer correlation coefficient set. After that, the rule of gather weighting coefficient Q is established to identify process fault. The application on Tennessee Eastman process illustrates that the PCC-SDG method can realize fault detection and isolation tasks in an effective pattern. Because its modeling and diagnosis procedures are simple and SDG can be readily probed for the root cause, the proposed method has an advantage in process supervision.关键词
故障检测与诊断/皮尔逊相关系数/SDG/多层相关系数集/聚集权重系数Q规则Key words
fault detection and diagnosis/Pearson correlation coefficient/SDG/multilayer correlation coefficient sets/rule of gather weighting coefficient Q分类
化学化工引用本文复制引用
董玉玺,李乐宁,田文德..基于多层优化PCC-SDG方法的化工过程故障诊断[J].化工学报,2018,69(3):1173-1181,9.基金项目
国家自然科学基金项目(21576143).supported by the National Natural Science Foundation of China(21576143). （21576143）
