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

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

Chinese Journal of Chemical Engineering ›› 2014, Vol. 22 ›› Issue (6): 643-650.DOI: 10.1016/S1004-9541(14)60087-2

• 催化、动力学与反应工程 • 上一篇下一篇

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

田文德¹, 胡明刚², 李传坤³

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

收稿日期:2013-08-13 修回日期:2013-11-13 出版日期:2014-06-28 发布日期:2014-06-06
通讯作者: LI Chunxi
基金资助:
Supported by the Shandong Natural Science Foundation (ZR2013BL008)

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

TIAN Wende¹, HU Minggang², LI Chuankun ³

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

Received:2013-08-13 Revised:2013-11-13 Online:2014-06-28 Published:2014-06-06
Supported by:
Supported by the Shandong Natural Science Foundation (ZR2013BL008)

摘要/Abstract

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 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.

关键词: fault prediction, dynamic model, grey model, time series model

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.

Key words: fault prediction, dynamic model, grey model, time series model

田文德, 胡明刚, 李传坤. 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.

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.

参考文献

1 Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N., "A review of process fault detection and diagnosis Part I: Quantitative model-based methods", Comput. Chem. Eng., 27, 293-311 (2003).

2 Min, H.K., Chang, K.Y., "Multivariate monitoring for time-derivative non-gaussian batch process", Korean J. Chem. Eng., 25, 947-954 (2008).

3 Bin Shams, M.A., Budman, H.M., Duever, T.A., "Fault detection, identification and diagnosis using CUSUM based PCA", Chem. Eng. Sci., 66 (20), 4488-4498 (2011).

4 Srinivasan, R., Qian, M.S., "Online fault diagnosis and state identification during process transitions using dynamic locus analysis", Chem. Eng. Sci., 61 (18), 6109-6132 (2006).

5 Zheng, X.P., Liu, M.T., "An overview of accident forecasting methodologies", J. Loss Prevent. Proc., 22 (4), 484-491 (2009).

6 Yoo, C.K., Kim, M.K., Hwang S.J., Jo, Y.M., Oh, J.M., "Online predictive monitoring and prediction model for a periodic process through multiway non-gaussian modeling", Chin. J. Chem. Eng., 16 (1), 48-51 (2008).

7 Kim, M.J., Jiang, R., Makis, V., Lee, C.G., "Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure", Eur. J. Oper. Res., 214 (2), 331-339 (2011).

8 Suo, R.X., Wang, F.L., "The application of combination forecasting model in Chinese energy consumption", Mathematics in Practice and Theory, 40 (18), 80-85 (2010).

9 Wu, Y.G., Zhu, X.F., Shi, B.H., "Control study of long time delay process based on grey predictive model", Control Engineering of China, 14 (3), 278-280 (2007).

10 Wang, H.Z., Yang, J.P., Yu, N., "A fault prediction based on a grey nonlinear regression model", Journal of Ordnance Engineering College, 22 (1), 46-48 (2010).

11 Wang, Z., Wang, Y., Zhang, J., "Grey correlation analysis of corrosion on oil atmospheric distillation equipment", In: Proceedings of the 5th International Conference on Fuzzy System and Knowledge Discovering, Yin, Y.L., ed., IEEE, Jinan, China, 13-17 (2008).

12 Gao, D., Wu, C.G., Zhang, B.K., Ma, X., "Signed directed graph and qualitative trend analysis based fault diagnosis in chemical industry", Chin. J. Chem. Eng., 18 (2), 265-276 (2010).

13 Zhang, X., Ma, S.L., Yan, W.W., Zhao, X., Shao, H.H., "A novel systematic method of quality monitoring and prediction based on FDA and kernel regression", Chin. J. Chem. Eng., 17 (3), 427-436 (2009).

14 Lahiri, S.K., Ghanta, K.C., "Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model", Chin. J. Chem. Eng., 16 (6), 841-848 (2008).

15 Liu, S.F., Forrest, J., "The role and position of grey system theory in science development", The Journal of Grey System, 9 (4), 351-356 (1997).

16 Hsu, C.C., Chen, C.Y., "Applications of improved grey prediction model for power demand forecasting", Energ. Convers. Manage., 44 (14), 2241-2249 (2003).

17 Mao, M.Z., Chirwa, E.C., "Application of grey model GM(1, 1) to vehicle fatality risk estimation", Technological and Social Change, 73 (5), 588-605 (2006).

18 Tian, W. D., Sun, S.L., "On-line dynamic model correction based fault diagnosis in chemical processes", The Chinese Journal of Process Engineering, 7 (5), 952-959 (2007).

19 Tian, W.D., Guo, Q.J., Sun, S.L., "Dynamic simulation based fault detection and diagnosis for distillation column", Korean J. Chem. Eng., 29, 9-17 (2012).

20 Isermann, R., "Model-based fault detection and diagnosis—Status and application", Annual Reviews in Control, 29, 71-85 (2005).

21 Zhang, Z.D., Hu, S.S., "A new method for fault prediction of model-unknown nonlinear system", Journal of the Franklin Institute, 345 (2), 136-153 (2008).

22 Frank, P.M., Ding, S.X., Marcu, T., "Model-based fault diagnosis in technical processes", Transactions of the Institute of Measurement and Control, 22 (1), 57-101 (2000).

23 Li, R.Y., Rong, G.Z., "Fault isolation by partial dynamic principal component analysis in dynamic process", Chin. J. Chem. Eng., 14 (4), 486-493 (2006).

24 Wang, Z.F., Yuan, J.Q., "Online supervision of penicillin cultivations based on rolling MPCA", Chin. J. Chem. Eng., 15 (1), 92-96 (2007).

25 Chiang, L.H., Russel, E.L., Braatz, R.D., "Parameter estimation", In: Fault Detection and Diagnosis in Industrial Systems, China Machine Press, Beijing, 179-189 (2003).

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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

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