%0 Journal Article %A FENG Liwei %A ZHANG Cheng %A LI Yuan %A XIE Yanhong %T DLNS-PCA-based fault detection for multimode batch process %D 2018 %R 10.11949/j.issn.0438-1157.20171629 %J CIESC Journal %P 3159-3166 %V 69 %N 7 %X

Modern industrial products often require multiple production stages, and the fault detection of multi-stage production process has become an important issue. Multi-stage process data have the characteristics of multi center, different structure of each stage and so on. Aiming at the characteristics, a fault detection method based on double local neighborhood standardization and principal component analysis (DLNS-PCA) is proposed. Firstly, the double local neighborhood set of the sample is found. Secondly, the standard samples are obtained by using the information of the double local neighborhood set. Finally, the PCA method is used to detect the fault on the standard sample set. The DLNS can move the data centers of each stage to the same point, and adjust the degrees of dispersion of data at each stage to make its approximately equal, then multi-stage process data is fused to a single modal data following multivariate Gauss distribution. A fault detection of penicillin simulation process was carried out. The results showed that DLNS-PCA has higher fault detection rate than PCA, KPCA and FD-kNN methods. DLNS-PCA method improves the efficiency of multi-stage process fault detection.

%U https://hgxb.cip.com.cn/EN/10.11949/j.issn.0438-1157.20171629