CIESC Journal ›› 2018, Vol. 69 ›› Issue (7): 3159-3166.doi: 10.11949/j.issn.0438-1157.20171629

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DLNS-PCA-based fault detection for multimode batch process

FENG Liwei1,2, ZHANG Cheng1,2, LI Yuan2, XIE Yanhong1,2   

  1. 1 College of Science, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China;
    2 Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China
  • Received:2017-12-12 Revised:2018-02-12 Online:2018-07-05 Published:2018-04-09
  • Supported by:

    supported by the National Natural Science Foundation of China (61490701,61673279), the Project of Education Department in Liaoning Province (L2015432) and the Natural Science Foundation of Liaoning Province (2015020164).

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Abstract:

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.

Key words: multi stage process, fault detection, model, principal component analysis, process control

CLC Number: 

  • TP277

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