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化工学报  2015 

基于统计量模式分析的T-KPLS间歇过程故障监控

DOI: 10.11949/j.issn.0438-1157.20141476, PP. 265-271

Keywords: 故障监控,核函数全影结构投影,统计量模式分析

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Abstract:

核函数的全影结构投影(totalkernelprojectiontolatentstructures,T-KPLS)最近在故障监控领域取得了广泛应用,其实质是对数据矩阵的协方差矩阵进行分解,没有利用数据的高阶统计量等有用信息,在进行特征提取时会造成数据有用信息的丢失,导致故障识别效果差。为了解决此问题,提出了统计量模式分析(statisticspatternanalysis,SPA)与核函数的全影结构投影法(totalkernelprojectiontolatentstructures,T-KPLS)相结合的多向统计量模式分析的核函数的全影结构投影法(multi-waystatisticspatternanalysistotalkernelprojectiontolatentstructures,MSPAT-KPLS)。该方法首先构造样本的不同阶次统计量,将数据从原始的数据空间映射到统计量样本空间,然后利用核函数将统计量样本空间映射到高维核空间并在质量变量的引导下将特征空间分为过程变量与质量变量相关、过程变量与质量变量无关、过程变量与质量变量正交和残差4个子空间;最后针对与质量变量相关和残差空间建立联合监控模型,当监控到有故障发生时进行故障变量追溯。最后将该方法应用到微生物发酵过程中,并与传统方法进行比较,发现该方法具有更好的监控性能。

References

[1]  Peng K X, Zhang K, Li G, et al. Contribution rate plot for nonlinear quality related fault diagnosis with application to the hot strip mill process [J]. Control Engineering Practice, 2013, 21(4): 360-369
[2]  He Q, Wang J. Statistics pattern analysis: a new process monitoring framework and its application to semiconductor batch processes [J]. American Institute of Chemical Engineers, 2011, 57(1): 107-121
[3]  Wang J, He Q. Multivariate statistical process monitoring based on statistics pattern analysis [J]. Industrial Engineering Chemical Research, 2010, 49(1): 7858-7869
[4]  Zhang Hanyuan(张汉元), Tian Xuemin(田学民), Deng Xiaogang(邓晓刚). Fault identification method based on SPA similarity factor [J]. CIESC Journal(化工学报), 2013, 64(12): 4503-4508
[5]  Chang Peng(常鹏),Wang Pu(王普),Gao Xuejin(高学金),et al. Batch process monitroing and quality prediction based on statistics pattern analysis and MKPLS [J]. Chinese Journal of Scientific Instrument(仪器仪表学报), 2014, 35(6): 1409-1416
[6]  Birol G, Undey C, Cinar A. A modular simulation package for fed-batch fermentation: penicillin production [J]. Computers and Chemical Engineering, 2002, 26(11): 1553-1565
[7]  Agudo D, Ferrer A, Ferrer J, et al. Multivariate SPC of a sequencing batch reactor for wastewater treatment [J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85(1): 82-93
[8]  Alcala C F, Qin S J. Analysis and generalization of fault diagnosis methods for process monitoring [J]. Journal of Process Control, 2011, 21(1): 322-330
[9]  Alcala C F, Qin S J. Reconstruction based contribution for process monitoring [J]. Automatica, 2009, 45(1): 1593-1600
[10]  Mori J C, Yu J. Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach [J]. Journal of Process Control, 2014, 24(1): 57-71
[11]  Xiong H S, Gong X C, Qu H B. Monitoring batch to batch reproducibility of liquid-liquid extraction process using in-line near-infrared spectroscopy combined with multivariate analysis [J]. Journal of Pharmaceutical and Biomedical Analysis, 2012, 70(11): 178-187
[12]  Jia Runda (贾润达), Mao Zhizhong (毛志忠), Wang Fuli (王福利). KPLS model based product quality control for batch processes [J]. CIESC Journal ( 化工学报), 2013, 64(4): 1332-1339
[13]  Stubbs S, Zhang J, Morris J L. Multiway interval partial least squares for batch process performance monitoring [J]. Industrial and Engineering Chemistry Research, 2014, 52 (35) : 12399-12407.
[14]  Geert G, Jef V, Jan F. Discriminating between critical and noncritical disturbances in (bio)chemical bach processes using multi-model fault detection and end-quality prediction [J]. Industrial and Engineering Chemistry Research, 2012, 51(1): 12375-1238
[15]  Naes T, Tomic O. Multi-block regression based on combination so for thogonalisation, PLS regressionand canonical correlation analysis [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 124: 32-42
[16]  Li G, Alcala C F, Qin S J, et al. Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models [J]. Industrial and Engineering Chemistry Research, 2010, 49(19):9175-9183
[17]  Zhou D, Li G, Qin S J. Total projection to latent structures for process monitoring [J]. American Institute of Chemical Engineers, 2010, 56(1): 168-178
[18]  Li G, Alcala C F, Qin S J, et al. Generalized reconstruction based contributions for output relevant fault diagnosis with application to the Tennessee Eastman process [J]. IEEE Transaction on Control Systems Technology, 2011, 19(5): 1114-1127
[19]  Jackson J E. A User's Guide to Principal Components[M]. New York: Wiley, 1991
[20]  Nomikos P, MacGregor J F. Monitoring batch process using multiway principal component analysis [J]. American Institute of Chemical Engineers, 1994, 40(8): 1361-1375

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