摘要
提出一种基于核主元分析(KPCA)的故障诊断方法,通过提取集成算子与非线性核函数计算后映射到高维空间的主元成分,有效地捕捉过程变量的非线性关系.对华能福州电厂烟气脱硫过程采集的数据进行传感器完全失效、偏差等故障实验仿真,结果表明,KPCA具有很好的故障诊断能力.
A fault diagnosis method using kernel principal component analysis（KPCA） is proposed to affectively capture the nonlinear relationship of the process variables,which computes principal component in high dimensional feature space by means of integral operators and nonlinear kernel functions.Employing the actual data from wet flue gas desulphurization system of Huaneng Fuzhou power plant,it＇s proved effectively to detect and identify the complete invalidation fault,fixed bias fault and so on.The result shows the KPCA method has good performance in fault detection and diagnosis.
出处
《福州大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第3期349-353,共5页
Journal of Fuzhou University(Natural Science Edition)
基金
福建省发改委产业技术开发专项资金资助项目(0803119)
