基于小波分析的控制圖模式識別研究.pdf

1、鄭州大學(xué)碩士學(xué)位論文基于小波分析的控制圖模式識別研究姓名：周昊飛申請學(xué)位級別：碩士專業(yè)：企業(yè)管理指導(dǎo)教師：劉玉敏201205AbSt憎ctAbstractThecontrolchartisarleffectivewayofmodemqualitymanagementrealtimequalitymonitoringanddiagnostictoolsfortheproductionprocess，Inparticularthecontr

2、olchartintheproductionprocessmonitoringsignificantlyincreasedthelevelofqualityassuranceofthemanufacturingprocessAlthoughthecontrolchartprinciplesandmethodsplayahugeroleinthefieldofqualitycontrol，howeversmallprobabilityev

3、entsprincipleasthetheoreticalbasisfortheabnormalpatternofcontrolchartmonitoringguidelines，difficulttoidentifyabnormalpaRernofthespike，steptrends，cyclesintheproductionprocess，whichgreatlyweakenedthemonitoringeffectoftheco

4、ntrolchartAtpresentthemethodofcombiningwaveletanalysisandneuralnetworktechnologyhasbecomeahotspotofthecontrolchartpatternrecognitionHowtousewaveletanalysismethodtoextractthecharacteristicsofabnormalcontrolchartpatterns，a

5、ndselectthecorrespondingBPneuralnetwork(ANN)controlchartpaaernrecognitionisthekeytoControlChartPaaernRecognitionBasedonWaveletAnalysisInthispaperwaveletanalysisandneuralnetworkastheoreticalbasisforthesystematicstudyofthe

6、controlchartpaRernrecognitionmethodusingwaveletanalysisFirstofaIl，thispaperbasedonthedomesticandforeignresearchesaboutcontrolchartpatternrecognition，waveletanalysisandneuralnetworktodefinetheabnormalpatternsofcontrolchar

7、tsSecondlyBasedOnthecontrolchartpatternrecognitionresearchOnedimensionaldiscretewaveletdecomposition(DWDtoextractfeaturesoftheControlChartPaRerndata，DataextractedfeaturesasinputofBPneuralnetworktoidentifythecontrolchartp

8、atterns，establishacontrolchartpatternrecognitionmodelbasedonwaveletanalysis，thatisDWNNcontrolchartpatternrecognitionmodelFurthermoreanalysisonthechoiceofthewaveletfunctionandtheparametersoftheBPneuralnetworkidentifierfin