基于MICA方法的间歇过程监控研究

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1、中国石油大学（华东）硕士学位论文基于MICA方法的间歇过程监控研究姓名：张晓玲申请学位级别：硕士专业：控制理论与控制工程指导教师：田学民20080501独立成分，并利用，2和SPE统计图监测过程中是否有故障发生。FS．MKICA方法不仅能提取间歇过程中的非线性特性，而且减少了基于全部样本建模的计算代价，对青霉素发酵过程的监控结果显示，该非线性算法比线性MICA方法检测故障时更灵敏。关键词：间歇过程，故障检测与诊断，多向独立成分分析，自适应算法，非线性BatchProcessMonitoringBased011MICAMethodsZhangXiaoling(ControlTheoryandCo

2、ntrolEngineering)DirectedbyProf．TianXueminAbstractprocesseshavebecomemoreandmoreimportantinmodemindustrialprocesses．Inensuringthesafetyandstabilityofbatchprocessesandhighqualityfmalproduct,on-linemonitoringandfaultdiagnosisinbatchprocessesemergeasanessentialandimportanttask．Asthedevelopmentofon-line

3、measurementinstrurnentsandcomputertechnology，largeamountsofprocessvariables’datacanbecollectedmoreeasilythanbefore．ThedataCanbeanalyzedtosupervisetheprocessbehavior,byminingthevaluableinformationandresources．Multi-wayprincipalcomponentanalysis(MPCA)andmulti-waypartialleastsquares(MPLS)，whichassumeth

4、atthevariablesmustsubjecttothenormaldistributionconditionandonlyutilizethesecond—orderstatisticalinformation,areusedmostwidelymultivariatestatisticaltechniqueinbatchprocessesmonitoring．Multi-wayindependentcomponentanalysis(MICA)，onetypeofmultivariatestatisticalmethodbasedonICAtechnique，isrecentlydev

5、elopedtoapplytothebatchprocessesmonitoring．ThismethodCantreat、析ththree—waydataofbatchprocessesmoreeffectivelybecauseitutilizesthehiIgh—orderstatisticalinformationandavoidstheassumptionofGaussiandistribution．Inaddition，theextractedlatentvariablesbyMICAarestatisticallyindependentwhileprincipalcomponen

6、tsgeneratedfromMPCAalemerelyde-correlated．Therefore，theindependentvariablesorcomponentscandescribetheprocessescharacteristicmoreintrinsicallythanMPCAorMPLS．Inthiswork，MICAbatchmonitoringmethodisdiscussedandconsideringthecharacteristicsofbatchprocesses，twonewkindsofmonitoringmethodsareproposedbasedon

7、MICA．Inviewofbatch—to—batchvariationinmostindustrialbatchprocesses，anadaptiveMICAmethodisproposedtocapturethedynamicvariationamongdifferentbatches．ThisapproachfirstestablishesanMICAmodelbasedonthehist