Weak fault monitoring method for batch process based on multi-model SDKPCA

• Published in: Chemometrics and intelligent laboratory systems 2012-08, Vol.118, p.1-12
• Main Authors: Wang, Ya-Jun, Jia, Ming-Xing, Mao, Zhi-Zhong
• Format: Article
• Language: English
• Subjects: Batch process monitoring; Fed-batch penicillin production; Hierarchical cluster; Multi-model single dynamic kernel PCA (M-SDKPCA); Weak fault detection
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2012.07.007

In industrial manufacturing, most batch processes have the dynamic and nonlinear features in nature. To ensure both quality consistency of the manufactured products and safe operation of this kind of batch process, a number of multivariate statistical analyses, including multiway principal component analysis (MPCA), batch dynamic kernel principal component analysis (BDKPCA), have been developed in recent years. However, these methods can't effectively detect the weak faults due to large fluctuations in the initial conditions, because the weak faults are submerged to the fluctuations in the poor initial conditions. In order to improve the performance of the weak fault detection, a new nonlinear dynamic batch process monitoring method, called multi-model single dynamic kernel principal component analysis (M-SDKPCA), is proposed in this paper. The multi-model methodology is based on BDKPCA. The method firstly integrates kernel PCA (KPCA) and auto-regressive moving average exogenous (ARMAX) time series model for each batch data at each stage to build SDKPCA. Then hierarchical clusters are obtained through load matrix similarity among SDKPCA models. At different stages, multiple model structures are constructed along with the variation of the cluster number. The monitoring method proposed in this paper was applied to fault detection for benchmark of fed-batch penicillin production. In both off-line analysis and on-line batch monitoring, the proposed approach shows better performance than MKPCA and BDKPCA. ► A new M-SDKPCA process monitoring method is proposed. ► Hierarchical cluster is obtained through load matrix similarity among SDKPCA models. ► Multi-model structures are constructed along with the variation of cluster number. ► Selection method of the best monitoring model from multiple models is described. ► The proposed approach shows better performance than MKPCA and BDKPCA.