摘要
针对变工况过程中传统主元分析方法的模型不适应问题,通过稳定性因子分析,剔除过渡过程数据,并用模糊聚类方法将不同稳态工况进行分类,利用动态主元模型方法根据工况类型建立不同的主元模型,并将该方法用于核动力装置传感器的故障检测,结果表明该方法能够适应变工况情况下的传感器故障检测,减少了故障的误检,并提高了检测灵敏度.
As to the maladjustment of model of traditional principal component analysis in changing condition process, different principal component models have been built by dynamic principal compo- nent analysis according to condition type, through stability factor analysis to eliminate the changing process data and condition classification of different steady conditions with the fuzzy-clustering meth- od. This method is applied to sensor fault detection for nuclear power plant . The result shows that it is fit for sensor fault detection in changing condition process, it reduces the chances of detection mis- takes and it improves the detection sensitivity.
出处
《武汉理工大学学报（交通科学与工程版）》
2012年第6期1184-1187,1191,共5页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
关键词
主元分析
变工况过程
稳定性因子
模糊聚类
故障检测
principal component analysis
changing condition process
stability factor
fuzzy-clustering fault detection
