收稿日期: 2008-06-30
修回日期: 2008-07-16
网络出版日期: 2009-03-15
基金资助
国家高技术研究发展计划(863计划)(2006AA04Z185);辽宁省教育厅项目(2005320)资助
Monitoring based on improved OFA-MPCA
Received date: 2008-06-30
Revised date: 2008-07-16
Online published: 2009-03-15
多向主元分析(MPCA)是利用多变量统计方法从纷杂的海量数据信息中提取出能够准确表征数据信息的几个主元,并通过投影法来降低数据的维数,主要应用于间歇生产过程中. 在实际的间歇生产过程中,由于各种原因导致各批次异步造成它们运行时间的不一致,而无法直接建立有效的统计模型,正交函数近似(OFA)是一种基于正交基的投影变换技术,通过对原始数据进行OFA处理后,可以用投影系数来描述原始数据所具有的特征,并且可以达到轨迹同步化和压缩数据量的目的. 对OFA法进行了部分改进,并结合MPCA法对典型的间歇过程——青霉素发酵过程进行了仿真研究. 结果表明,改进的OFA计算速度有了极大的提高,且改进的OFA-MPCA法能完好地对各批次进行同步、建模并得出准确的监视结果.
边福强 , 高翔 , 苑明哲 . 基于改进的OFA-MPCA的监控方法[J]. 中国科学院大学学报, 2009 , 26(2) : 209 -214 . DOI: 10.7523/j.issn.2095-6134.2009.2.009
Multiway Principal Component Analysis (MPCA) is a multivariable statistical approach, which can extract several principal components from the numerous of data to express the data information well, and is mainly used in batch process. In practice, for many reasons, the runtime of each batch is different from others so that the effective statistical model can not be built directly. Orthonormal Function Approximation (OFA) is a technique of project transformation based on orthonormal base, after OFA we can use the projection coefficient to express the characteristics of the original data and synchronize the trajectories of each historical batch and reduce the dimension. This paper presents some improvement on the OFA and combined the MPCA to model and monitor the typical batch process——Penicillin fermentation process. The simulation results show that the improved OFA can deal with data more quickly and the improved OFA-MPCA is able to synchronize the trajectories of all the batches, and monitor the batches perfectly.
Key words： orthonormal function approximation; MPCA; batch process; pensim
[1] Nomikos P, MacGregor JF. Monitoring batch process using multi-way principle component analysis. Journal of AICHE, 1994, 40 (8):1361~1369
[2] Shah SL, Randy M, Takada H, et al. Modelling and control of a tubular reactor: A PCA-based approach. In: Proceedings of the 5th IFAC Symposium on Dynamics and Control of Process Systems. Corfu, Greece, 1998. 17~22
[3] Lakshminarayanan S, Guidi RD, Shah SL, et al. Monitoring batch processes using multivariate statistical tools: extensions and practical issues. IFAC Triennial World Cong. San Francisco, 1996. 241~246
[4] Kourti T, MacGregor JF. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 1996,28(4):409~427
[5] Kassidas A, MacGregor, et al. Synchronization of batch trajectories using dynamic time warping. Journal of AIChE, 1998, 44 (4): 864~875
[6] Chen JH, Liu JL. Post analysis on different operating time Processes using orthonormal function approximation and multiway principle component analysis. Journal of Process Control, 2000,10 (5):411~418
[7] Chen JH, Liu JL. Multivariate calibration models based on functional space and partial least square for batch processes. IFAC (ChemFas-4) Conference. Chejudo, Korea, 2001. 161~166
[8] Jiang TH, et al. Fault detection and diagnosis in industrial systems. China Machine Press, 2003
[9] Zhang J,Yang XH. Multivariable statistal process control. Chemical Industrial Publication House, 2000
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