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.