一种主元分析方法在聚合生产过程故障监测与诊断中的应用

对聚合生产过程中的操作变量进行主元分析，提取过程变量的主要特征，建立主元模型，可实现对多变量过程数据的压缩．在不减少原信息主要特征的基础上，有效地去除了测量噪声．将主元分析应用到聚合反应生产过程故障诊断系统中，大大减少了监测变量个数，能够实现对过程状态的有效监控；异常状态可根据变量对统计量贡献值的大小来确定故障源．

An Application of PCA for Monitoring and Diagnosing Fault in a Chemical Polymeric Process

The model of principal components is built up by applying a method of principal component analysis and by extracting the principal character of process variable to condense process data of multi-variables in a process of chemical polymeric production. The method can efficiently eliminate measure noise while all main characters of original information remain entirely. It can cut down the number of monitoring variables in the system of fault diagnosis for the polymerization reaction. Furthermore, it can monitor the process efficiently. As to the fault data, the source of fault can be defined by the contributing degree of the variable to the statistics.