基于Fisher子空间特征提取的间歇过程监控和故障诊断

• SYSTEM ENGINEERING • 上一篇下一篇

基于Fisher子空间特征提取的间歇过程监控和故障诊断

赵旭; 阎威武; 邵惠鹤

Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-12-28 发布日期:2006-12-28
通讯作者: 赵旭

Monitoring and fault diagnosis for batch process based on feature extract in Fisher subspace

ZHAO Xu; YAN Weiwu; SHAO Huihe

Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

Received:1900-01-01 Revised:1900-01-01 Online:2006-12-28 Published:2006-12-28
Contact: ZHAO Xu

摘要/Abstract

摘要： Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

关键词: batch monitoring;fault diagnosis;feature extract;Fisher discriminant analysis;penicillin fermentation process

Abstract: Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

Key words: batch monitoring, fault diagnosis, feature extract, Fisher discriminant analysis, penicillin fermentation process

赵旭, 阎威武, 邵惠鹤. 基于Fisher子空间特征提取的间歇过程监控和故障诊断[J]. , DOI: .

ZHAO ,Xu, YAN ,Weiwu, SHAO ,Huihe. Monitoring and fault diagnosis for batch process based on feature extract in Fisher subspace[J]. , DOI: .

参考文献

1    Chen, J.H., Liu, K.C., “On-line batch process monitoring using dynamic PCA and dynamic PLS models”, Chem. Eng. Sci., 57, 63—75(2002).
2    Nomikos, P., MacGregor, J.F., “Monitoring batch proc-esses using multiway principal component analysis”, AIChE J., 40, 1361—1375(1994).
3    Nomikos, P., MacGregor, J.F., “Multivariate SPC charts for monitoring batch processes”, Technometrics, 37,   41—59(1995).
4    Tates, A.A., Louwerse, D.J., Smilde, A.K., “Monitoring a PVC batch process with multivariate statistical process control charts”, Ind. Eng. Chem. Res., 38, 4769—4776(1999).
5    Ricard, B., Smilde, A.K., “Monitoring and diagnosing batch processes with multiway covariates regression models”, AIChE J., 45, 1504—1520 (1999).
6    Lennox, B., Montague, G.A., Hiden, H.G., Kornfeld, G., Goulding, P.R., “Process monitoring of an industrial fed-batch fermentation”, Biotechnol. Bioeng., 74, 125—135(2001).
7    Undey, C., Ertunc, S., Cinar, A., “Online batch/fed-batch process performance monitoring, quality prediction, and variable contribution analysis for diagnosis”, Ind. Eng. Chem. Res., 42, 4645—4658(2003).
8    Lee, J.M., Yoo, C.K., Lee, I.B., “On-line batch process monitoring using a consecutively updated multiway principal component analysis model”, Comp. Chem. Eng., 27, 1903—1912(2003).
9    Lee, J.M., Yoo, C.K., Lee, I.B., “Fault detection of batch processes using multiway kernel principal component analysis”, Comp. Chem. Eng., 28, 1837—1847(2004).
10    He, N., Wang, S.Q., Xie, L., “An improved adaptive multi-way principal component analysis for monitoring streptomycin fermentation process”, Chin. J. Chem. Eng., 12, 96—101(2004).
11    Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification, 2nd ed., Wiley, New York (2001).
12    Chiang, L.H., Russell, E.L., Braatz, R.D., “Fault diagno-sis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis”, Chemometrics Int. Lab. Sys., 50, 243—252 (2000).
13    He, Q.P., Qin, S.J., “A new fault diagnosis method using fault directions in fisher discriminant analysis”, AIChE J., 51, 555—571(2005).
14    Lu, N.Y., Wang, F.L., Gao, F.R., “Combination method of principal component and wavelet analysis for multi-variate process monitoring and fault diagnosis”, Ind. Eng. Chem. Res., 42, 4198—4207(2003).
15    Kano, M., Hasebe, S., Hashimoto, I., “Statistical process monitoring based on dissimilarity of process data”, AIChE J., 48, 1231—1240(2002).
16    Chiang, L.H., Russell, E.L., Braatz, R.D., Fault Detec-tion and Diagnosis in Industrial Systems, Springer, Hong Kong (2001).
17    Birol, G., Undey, C., Cinar, A., “A modular simulation package for fed-batch fermentation: Penicillin produc-tion”, Comp. Chem. Eng., 26, 1553—1565(2002).