Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

田文德; 胡明刚; 李传坤

doi:10.1016/S1004-9541(14)60087-2

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Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

催化、动力学与反应工程 | Updated：2026-01-06

- Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes
- Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes
- 中国化学工程学报（英文版） 2014年22卷第6期页码：643-6501004-954160087-2
- Affiliations：
  
  1. Zibo Weichuang Petrochemical Design Co., Ltd,Zibo,China,255400
  2. State Key Laboratory of Chemicals Safety, Qingdao Safety Engineering Institute, SINOPEC,Qingdao,China,266071
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(14)60087-2
  中图分类号：
- 纸质出版：2014
- Accepted：
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田文德, 胡明刚, 李传坤. Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes[J]. 中国化学工程学报（英文版）, 2014,22(6):643-6501004-954160087-2.

TIAN Wende, HU Minggang, LI Chuankun . Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes[J]. Chin.J.Chem.Eng., 2014, 22(6): 643-650.
田文德, 胡明刚, 李传坤. Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes[J]. 中国化学工程学报（英文版）, 2014,22(6):643-6501004-954160087-2. DOI： 10.1016/S1004-9541(14)60087-2.

TIAN Wende, HU Minggang, LI Chuankun . Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes[J]. Chin.J.Chem.Eng., 2014, 22(6): 643-650. DOI： 10.1016/S1004-9541(14)60087-2.

摘要

Abstract

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method

historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method

historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

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