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1、中國(guó)石油大學(xué)（華東）碩士學(xué)位論文基于MICA方法的間歇過程監(jiān)控研究姓名：張曉玲申請(qǐng)學(xué)位級(jí)別：碩士專業(yè)：控制理論與控制工程指導(dǎo)教師：田學(xué)民20080501獨(dú)立成分，并利用，2和SPE統(tǒng)計(jì)圖監(jiān)測(cè)過程中是否有故障發(fā)生。FS．MKICA方法不僅能提取間歇過程中的非線性特性，而且減少了基于全部樣本建模的計(jì)算代價(jià)，對(duì)青霉素發(fā)酵過程的監(jiān)控結(jié)果顯示，該非線性算法比線性MICA方法檢測(cè)故障時(shí)更靈敏。關(guān)鍵詞：間歇過程，故障檢測(cè)與診斷，多向獨(dú)立成分分析，自適應(yīng)算法，非線性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
