| Fault prediction based on dynamic model and grey time series model in chemical processes |
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| 作者姓名： | 田文德 ;胡明刚 ;李传坤 |
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| 作者单位： | 1. College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China;
2. Zibo Weichuang Petrochemical Design Co., Ltd, Zibo 255400, China;
3. State Key Laboratory of Chemicals Safety, Qingdao Safety Engineering Institute, SINOPEC, Qingdao 266071, China |
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| 基金项目： | Supported by the Shandong Natural Science Foundation （ZR2013BL008）. |
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| 摘 要： | 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 in-troduced 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 re-trieve 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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| 关 键 词： | fault prediction dynamic model grey model time series model |
| 收稿时间： | 2013-08-13 |
Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes |
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| TIAN Wende,HU Minggang,LI Chuankun.Fault prediction based on dynamic model and grey time series model in chemical processes[J].Chinese Journal of Chemical Engineering,2014,22(6):643-650. |
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| Authors: | TIAN Wende HU Minggang LI Chuankun |
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| Affiliation: | 1. College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China;
2. Zibo Weichuang Petrochemical Design Co., Ltd, Zibo 255400, China;
3. State Key Laboratory of Chemicals Safety, Qingdao Safety Engineering Institute, SINOPEC, Qingdao 266071, China |
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| 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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| Keywords: | fault prediction dynamic model grey model time series model |
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