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基于贝叶斯网络的铝型材挤压过程异常检测.docx

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基于贝叶斯网络的铝型材挤压过程异常检测 Title:AnomalyDetectioninAluminumExtrusionProcessbasedonBayesianNetworks Abstract: Thealuminumextrusionprocessisextensivelyusedinvariousindustries,includingautomotive,aerospace,andconstruction.However,detectinganomaliesintheextrusionprocessischallengingduetoitscomplexanddynamicnature.Inthispaper,weproposeanovelapproachusingBayesianNetworks(BN)todetectanomaliesinthealuminumextrusionprocess.TheBNmodelcapturestherelationshipsbetweendifferentvariablesinvolvedintheprocessandprovidesaprobabilisticframeworkforanomalydetection.Experimentalresultsdemonstratetheeffectivenessandefficiencyoftheproposedapproachforreal-timemonitoringanddetectionofanomaliesinthealuminumextrusionprocess. 1.Introduction: Aluminumextrusionisamanufacturingprocessinwhichaluminumbilletsarepressedthroughadietoproducevariousshapesofaluminumprofiles.Thisprocessiscriticalforachievingprecisionanddesiredmechanicalpropertiesofaluminumprofiles.However,deviationsfromtheoptimalextrusionparameterscanresultindefectsoranomaliesinthefinalproducts.Detectingandaddressingtheseanomaliesinreal-timeiscrucialtoensurethequalityofaluminumprofilesandreduceproductrejectionrates.Inthispaper,weproposeaBayesianNetwork-basedapproachforanomalydetectioninthealuminumextrusionprocess. 2.RelatedWork: Existingliteratureonanomalydetectioninmanufacturingprocessesprimarilyfocusesontraditionalstatisticalmethods,machinelearningtechniques,andexpertknowledge-basedsystems.However,theseapproachesoftensufferfromlimitationssuchasdependenceonpredefinedparameters,lackofabilitytohandlecomplexrelationshipsintheprocess,andtheneedforsignificantmanualintervention.BayesianNetworks,ontheotherhand,provideapowerfulframeworktomodelcomplexdependenciesanduncertaintiesintheprocessvariables,makingthemsuitableforanomalydetection. 3.ProposedApproach: Theproposedapproachinvolvesthefollowingsteps: a.DataCollection:Wecollectreal-timesensordatafromvariouspointsinthealuminumextrusionprocess,includingtemperature,pressure,velocity,andpowerconsumption. b.