宋励嘉,童楚东

• 1:宁波大学 信息科学与工程学院，浙江 宁波 315211

摘要(Abstract)：

传统主元分析（principal component analysis，PCA）算法在实施投影变换挖掘潜在特征成分时，不同投影变换方向对原始数据不同测量变量赋予了不同的权重值。而从故障检测的角度出发，各个测量变量的重要性程度是等同的。因此，传统PCA方法的过程监测性能还有待商榷。为克服这一缺陷，借鉴基于机理模型故障检测方法生成误差的思路，提出一种基于缺失变量估计误差的工业过程监测方法。首先，通过逐一假设每个测量变量缺失后，利用PCA模型中处理缺失变量的迭代算法（iterative algorithm，IA）推测出相应缺失变量的估计值。然后，以缺失变量的实际值与估计值之间的误差作为被监测对象实施在线故障检测。通过逐一假设变量缺失的方式，等同地对待了每个测量变量。而利用误差实施监测则可以在缺失变量前提下，通过监测误差的变化反映出该缺失变量对PCA模型中特征成分的影响程度。最后，通过在TE过程上的对比验证了该方法的优越性与可行性。

关键词(KeyWords)： 主成分分析;故障检测;缺失变量;误差生成

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（61503204，61773225）；浙江省自然科学基金（LY16F030001）。

作者(Author): 宋励嘉,童楚东

DOI: 10.3969/j.issn.1003-9015.2019.01.022

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