Process Systems Engineering

Data-based linear Gaussian state-space model for dynamic process monitoring

  1. Qiaojun Wen,
  2. Zhiqiang Ge*,
  3. Zhihuan Song*

Article first published online: 19 MAR 2012

DOI: 10.1002/aic.13776

Issue

AIChE Journal

AIChE Journal

Volume 58, Issue 12, pages 3763–3776, December 2012

Additional Information

How to Cite

Wen, Q., Ge, Z. and Song, Z. (2012), Data-based linear Gaussian state-space model for dynamic process monitoring. AIChE J., 58: 3763–3776. doi: 10.1002/aic.13776

Author Information

  1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, Zhejiang, P.R. China

*State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, Zhejiang, P.R. China

Publication History

  1. Issue published online: 8 NOV 2012
  2. Article first published online: 19 MAR 2012
  3. Accepted manuscript online: 13 FEB 2012 11:03AM EST
  4. Manuscript Revised: 9 FEB 2012
  5. Manuscript Received: 2 JUN 2011

Funded by

  • National Basic Research Program of China (973 Program). Grant Number: 2012CB720500
  • National Natural Science Foundation of China. Grant Numbers: 60974056, 61004134
  • Fundamental Research Funds for the Central Universities

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Keywords:

  • probabilistic;
  • dynamic;
  • linear Gaussian state-space model;
  • fault detection, fault identification

Abstract

This article develops a data-based linear Gaussian state-space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state-space model, and an iterative expectation-maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012

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