化工学报2012，Vol.63Issue(3)：873-880,8.DOI:10.3969/j.issn.0438-1157.2012.03.028
基于距离空间统计量分析的多模态过程无监督故障检测
Unsupervised fault detection for muitimode processes using distance space statistics analysis
摘要
Abstract
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 κ-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 κ-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.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Key words
multimode processes/fault detection/statistics analysis/principal component analysis/distance space分类
信息技术与安全科学引用本文复制引用
马贺贺,胡益,侍洪波..基于距离空间统计量分析的多模态过程无监督故障检测[J].化工学报,2012,63(3):873-880,8.基金项目
上海市重点学科建设项目(B504) （B504）
国家自然科学基金项目(61074079). （61074079）
