【摘要】 主元分析方法 (PCA)是基于多元统计分析的过程监测和故障诊断手段。在假设过程只存在传感器故障的情况下 ,系统地分析了PCA方法在传感器典型故障下的检测行为。首先导出了HotellingT2 和Q两个检测统计量在传感器不同故障下的变化关系和规律 ,然后从理论上给出了每个传感器故障的可检测性条件。最后通过火电厂锅炉过程中传感器故障检测实例验证了所得到的结论更多还原

【Abstract】 Principle component analysis is an effective multivariate statistical method for process monitoring and fault diagnosis. In this paper, a systematical analysis of detection behaviors of PCA for various sensor faults is presented. The characters of two basic detection statistics, Hotelling T 2 and Q are analyzed under different sensor fault types. Then fault detectablity for every individual sensor is given. The validity of analytical results is demonstrated by a real-world application to a boiler system.更多还原