文献类型：期刊文献

中文题名：基于距离空间统计量分析的多模态过程无监督故障检测

英文题名：Unsupervised fault detection for multimode processes using distance space statistics analysis

作者：马贺贺[1];胡益[1];侍洪波[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2012

卷号：63

期号：3

起止页码：873

中文期刊名：化工学报

外文期刊名：CIESC Journal

收录：CSTPCD;;EI(收录号：20121314899723);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

基金：上海市重点学科建设项目(B504);国家自然科学基金项目(61074079)~~

语种：中文

中文关键词：多模态过程;故障检测;统计量分析;主元分析;距离空间

外文关键词：multimode processes; fault detection; statistics analysis; principal component analysis; distance space

摘要：工业过程往往运行于多个生产模态,针对多模态过程数据的空间分布特点,提出了一种新的基于样本距离空间统计量分析的故障检测方法 (DSSA)。首先用每一个样本与其训练集样本中的邻居之间的k个最近邻距离之差来表示该样本,将样本从原始变量空间映射到对应的距离空间中。然后在距离空间中通过移动窗口的方式计算各阶统计量,最后对由各阶统计量组成的统计量样本进行主元分析(PCA)。将DSSA方法、PCA方法以及另一种基于k近邻规则的多模态故障检测方法 (FD-kNN)应用于TE过程中,仿真结果表明DSSA方法对多模态故障检测更为有效。
Industrial processes are often operated under different modes. However, most of the multivariate statistical process monitoring （MSPM） methods, such as principal component analysis （PCA） which are effective in single mode process, do not perform well in multimode process. A novel multimode fault detection approach named distance space statistics analysis （DSSA） was proposed. First, every sample was represented by the deviations of its k-nearest distances between itself and its neighbor in the training data. All the samples were mapped from the original space into the distance space by this way. Then, different order statistics of the distance samples in a moving window were calculated in the distance space. Finally, principal component analysis （PCA） was used to analyze the new statistics samples. The proposed method, PCA method and a multimode fault detection method using k-nearest neighbor rule （FD-kNN） were applied to the Tennessee Eastman （TE） benchmark process. The comparison of monitoring results showed that the proposed method was superior to the PCA and FD-kNN for fault detection of the multimode process.