SCI和EI收录∣中国化工学会会刊

中国化学工程学报 ›› 2021, Vol. 34 ›› Issue (6): 116-124.DOI: 10.1016/j.cjche.2020.10.030

• Process Systems Engineering and Process Safety • 上一篇    下一篇

Local component based principal component analysis model for multimode process monitoring

Yuan Li, Dongsheng Yang   

  1. Department of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
  • 收稿日期:2020-06-30 修回日期:2020-09-18 出版日期:2021-06-28 发布日期:2021-08-30
  • 通讯作者: Yuan Li
  • 基金资助:
    The authors would like to acknowledge that the National Natural Science Foundation of China (61673279).

Local component based principal component analysis model for multimode process monitoring

Yuan Li, Dongsheng Yang   

  1. Department of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
  • Received:2020-06-30 Revised:2020-09-18 Online:2021-06-28 Published:2021-08-30
  • Contact: Yuan Li
  • Supported by:
    The authors would like to acknowledge that the National Natural Science Foundation of China (61673279).

摘要: For plant-wide processes with multiple operating conditions, the multimode feature imposes some chal lenges to conventional monitoring techniques. Hence, to solve this problem, this paper provides a novel local component based principal component analysis (LCPCA) approach for monitoring the status of a multimode process. In LCPCA, the process prior knowledge of mode division is not required and it purely based on the process data. Firstly, LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture (FGMM). Then, calculating the posterior probability is applied to determine each sample belonging to which local component. After that, the local component information (such as mean and standard deviation) is used to standardize each sample of local component. Finally, the standardized samples of each local component are combined to train PCA monitoring model. Based on the PCA monitoring model, two monitoring statistics T2 and SPE are used for monitoring multimode pro cesses. Through a numerical example and the Tennessee Eastman (TE) process, the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

关键词: Principal component analysis, Finite Gaussian mixture model, Process monitoring, Tennessee Eastman (TE) process

Abstract: For plant-wide processes with multiple operating conditions, the multimode feature imposes some chal lenges to conventional monitoring techniques. Hence, to solve this problem, this paper provides a novel local component based principal component analysis (LCPCA) approach for monitoring the status of a multimode process. In LCPCA, the process prior knowledge of mode division is not required and it purely based on the process data. Firstly, LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture (FGMM). Then, calculating the posterior probability is applied to determine each sample belonging to which local component. After that, the local component information (such as mean and standard deviation) is used to standardize each sample of local component. Finally, the standardized samples of each local component are combined to train PCA monitoring model. Based on the PCA monitoring model, two monitoring statistics T2 and SPE are used for monitoring multimode pro cesses. Through a numerical example and the Tennessee Eastman (TE) process, the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

Key words: Principal component analysis, Finite Gaussian mixture model, Process monitoring, Tennessee Eastman (TE) process