基于廣義S變換的裂縫分頻邊緣檢測方法

基于廣義S變換的裂縫分頻邊緣檢測方法Abstract

Inthispaper,weproposeacrackfrequencyedgedetectionmethodbasedonthegeneralizedStransform.ThegeneralizedStransformisatime-frequencyanalysistoolthatcaneffectivelycapturethefrequencycomponentsofnon-stationarysignals.ByusingthepropertiesofthegeneralizedStransform,wecanextractthefrequencyfeaturesofcracks,whichcanbeusedtodetecttheedgesofcracks.Theproposedmethodhasbeentestedonanumberofsyntheticandrealcrackimages,andtheexperimentalresultsshowthattheproposedmethodcaneffectivelydetecttheedgesofcracksandhasabetterperformancethanthetraditionaledgedetectionmethods.

Introduction

Crackdetectionisanimportanttaskinmanyfields,suchasstructuralhealthmonitoring,defectdetectioninmaterials,andgeologicalexploration.Detectingtheedgesofcrackscanprovideusefulinformationforcrackcharacterizationandquantification.Traditionaledgedetectionmethods,suchastheSobeloperator,Cannyedgedetector,andLaplaceoperator,arewidelyusedforcrackedgedetection.However,thesemethodshavelimitationswhendealingwithnon-stationarysignals,suchasthoseproducedbycracks.Inrecentyears,time-frequencyanalysistoolshavebeendevelopedtoanalyzenon-stationarysignals.TheStransform,whichcananalyzethetime-varyingfrequencycomponentsofasignal,hasbeenusedincrackdetectionresearch.However,thetraditionalStransformhaslimitationsinanalyzingsignalswithdiscontinuities.

ThegeneralizedStransformisatime-frequencyanalysistoolthathasbeendevelopedtoovercomethelimitationsofthetraditionalStransform.ThegeneralizedStransformcaneffectivelyanalyzenon-stationarysignalswithdiscontinuities,suchasthoseproducedbycracks.Inthispaper,weproposeacrackfrequencyedgedetectionmethodbasedonthegeneralizedStransform.Theproposedmethodcandetecttheedgesofcracksbyextractingthefrequencycomponentsofthesignal.Theexperimentalresultsshowthattheproposedmethodhasabetterperformancethantraditionaledgedetectionmethods.

Method

Theproposedmethodconsistsofthefollowingsteps:

1)Imagepreprocessing:Thecrackimageispreprocessedtoremovenoiseandenhancethecontrastoftheimage.

2)GeneralizedStransform:ThepreprocessedimageistransformedusingthegeneralizedStransform,whichcananalyzethetime-varyingfrequencycomponentsofthesignal.ThepropertiesofthegeneralizedStransformareusedtoextractthefrequencycomponentsofthecracksignals.

3)Frequencyfeatureextraction:ThefrequencycomponentsofthecracksignalsareextractedfromthegeneralizedStransform.

4)Thresholding:Athresholdisappliedtothefrequencyfeaturestodetecttheedgesofthecracks.

5)Edgelinking:Thedetectededgesarelinkedtoformacrackedge.

ExperimentalResults

Toevaluatetheperformanceoftheproposedmethod,wetesteditonanumberofsyntheticandrealimageswithcracks.Theexperimentalresultsshowthattheproposedmethodcaneffectivelydetecttheedgesofthecracksandhasabetterperformancethantraditionaledgedetectionmethods.

Conclusion

Inthispaper,weproposedacrackfrequencyedgedetectionmethodbasedonthegeneralizedStransform.Theproposedmethodcaneffectivelydetecttheedgesofcracksbyextractingthefrequencyfeaturesofthesignal.Theexperimentalresultsshowthattheproposedmethodhasabetterperformancethantraditionaledgedetectionmethods.Theproposedmethodhaspotentialapplicationsincrackdetectioninmaterials,structuralhealthmonitoring,andgeologicalexploration.Theproposedmethodhassomeadvantagesovertraditionaledgedetectionmethods.Firstly,thegeneralizedStransformcaneffectivelyanalyzethetime-varyingfrequencycomponentsofthesignal,whichisessentialfordetectingcrackswithvaryingwidthsanddepths.Secondly,themethoddoesnotrequirepriorknowledgeaboutthecrackshapeorsize,whichmakesitmoreflexibleandapplicabletoawiderangeofcrackdetectionscenarios.Thirdly,thefrequencyfeaturesextractedfromthegeneralizedStransformprovideareliablebasisforthresholdingandedgelinking,whichcanreducefalsepositivesandimprovetheaccuracyofthedetectededges.

However,theproposedmethodalsohassomelimitations.Firstly,thecomputationalcostofthegeneralizedStransformishigherthanthatoftraditionaledgedetectionmethods,whichcanaffectthereal-timeperformanceofthemethodinsomeapplications.Secondly,themethodissensitivetonoise,andfurtherstudiesareneededtoimprovethenoiserobustnessofthemethod.

Inconclusion,theproposedcrackfrequencyedgedetectionmethodbasedonthegeneralizedStransformisapromisingapproachforcrackdetectioninmaterials,structuralhealthmonitoring,andgeologicalexploration.Furtherstudiesareneededtooptimizethemethodandexploreitspotentialapplicationsinotherfields.TofurtherimprovetheproposedcrackfrequencyedgedetectionmethodbasedonthegeneralizedStransform,severalresearchdirectionscanbeexplored.Firstly,thenoiserobustnessofthemethodcanbeimprovedbyusingnoisereductiontechniques,suchaswaveletdenoisingandadaptivefiltering.Furthermore,thescalingparameterofthegeneralizedStransformcanbeoptimizedtobalancebetweenthetimeandfrequencyresolution,whichcanimprovetheaccuracyoftheedgedetection.

Secondly,theproposedmethodcanbeextendedtodetectothertypesofdefects,suchasdelaminationandcorrosion,byanalyzingtheirspecificfrequencycharacteristics.Forexample,delaminationincompositematerialscanbedetectedbyanalyzingthehigh-frequencymodesofthevibrationalresponse,whilecorrosioninmetalstructurescanbedetectedbyanalyzingthelow-frequencymodesoftheelectrochemicalimpedancespectrum.

Thirdly,theproposedmethodcanbecombinedwithotherimagingtechniques,suchasopticalimagingandultrasoundimaging,toprovideamorecomprehensiveandaccuratediagnosisofthedefects.Forexample,cracksincivilstructurescanbedetectedbycombiningacousticemissionandopticalimaging,whichcanprovideinformationonthedepthandlocationofthecracks.

Finally,theproposedmethodcanbeappliedtoreal-timemonitoringandearlywarningofthedefects,whichcanpreventcatastrophicfailuresandreducemaintenancecosts.Forexample,crackpropagationinaircraftcomponentscanbemonitoredbyembeddingsensorsanddataacquisitionsystems,whichcanprovidereal-timefeedbackonthehealthstatusofthecomponents.

Insummary,theproposedcrackfrequencyedgedetectionmethodbasedonthegeneralizedStransformhasgreatpotentialforcrackdetectionandotherdefectdiagnosisinvariousfields.Furtherresearchisneededtooptimizethemethodandexploreitspracticalapplications.Additionally,theproposedcrackfrequencyedgedetectionmethodcanbeintegratedwithmachinelearningalgorithmstoenhanceitsabilitytorecognizeandclassifydifferenttypesofdefectsautomatically.Bytrainingthemachinelearningmodelswithlabeleddata,thesystemcannotonlydetectdefectsbutalsoclassifythemaccordingtotheirseverityandlocation,whichiscriticalforproactivemaintenanceandmonitoring.

Furthermore,theproposedmethodcanbeutilizedforanalysisoflong-termstructuralhealthmonitoringdata.Detectingsmallcracksordefectsatanearlystagecanhelppreventfurtherdamageandreducetheriskofcatastrophicfailure.Themethodcanbeusedtoevaluatethegrowthrateofthecracksandtoforecastthetimeofpossiblefailure.

Moreover,theproposedmethodcanbeusedforqualitycontrolinmanufacturingprocesses.Cracksordefectscanbedetectedandcorrectedinreal-time,avoidingcostlyreworkorrejectionoftheproduct.Withthistechnique,manufacturerscanimprovetheirproductionsystems,resultinginacost-effectiveandquality-controlledmanufacturingprocess.

Finally,theproposedmethodcanbeintegratedintovariousnon-destructivetestingtechniquessuchasultrasonictesting,eddycurrenttesting,X-rayandCTimagingtoimprovetheirefficiencyandsensitivitytodefects.Byaddingtheproposedmethodtothesetechniques,thesystemcanprovidehigherresolutionimagesanddetectdefectsthatmaynotbevisiblewithcurrentmethods.

Inconclusion,thepro