基于粒子群算法和小波理论的机械故障诊断

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1、ABSTRACTABSTRACTParticleswarmoptimization(PSO)isanoptimizationtechniquebasedonswarmintelligence.Thealgorithmprovidesefficientsolutionsforoptimizationproblemsthroughintelligencegeneratedfromcomplexactivitiessuchascooperationandcompetitionamongindividual

2、sinthebiologiccolony．Intheoptimization,themathematicalmodelisstructuredaccordingtotheengineeringproblemsandthespecificmeaningofthemathematicalmodelandthescopeoftheparametersinthemodelshouldbefullyunderstood.Afterthat,theparametersinvolvedintheparticles

3、warmopitimizationareinitiated,andthen,thefitnessfunctionwouldbeproposedbasedontheengineeringpractice.Thenthefitnessfunctionisusedtoevaluatetheparameterswhethertheparametersaresuitableornot.Comparedwiththetraditionaloptimizedalgorithm,theparticleswarmal

4、gorithminthemulti-dimensionalfunctionoptimization,thedynamicgoalseekstheexcellentaspecttohavetheconvergenceratetobequick,thesolutionqualityishigh,andsoonmerits,soithasbeenwidelyusedinengineeringpractice.Basedontheresearchofparticleswarmoptimizationandt

5、hewavelettheory,thismethodhasbeenappliedtodiagnosethegearboxfaultconditions.Basedonthenonlinearandnon-stationaryofgearboxfaultvibrationsignal,themathematicalmodelofthevibrationsignalbasedontheadaptiveMorletwaveletisproposedtoextractthegearboxfaultchara

6、cteristicsinformation.Theadaptivewavelettransform(AWT)ensurethatthemodelparametersoptimizedbytheparticleswarmoptimizationcorrespondtotheadaptivewaveletparametersandhasgoodperformanceinbothtimeandfrequencydomain.Forthegearboxfaultdiagnosisbasedonthepart

7、icleswarmoptimizationandthewavelettheory,firstly,theoriginalsignalisanalyzedbytheHilberttransformtogetthesignalenvelope.Thentheparametersinvolvedintheadaptivewaveletareproposed.Andthenthemethodbasedontheparticleswarmoptimizationandtheleastmeansquareerr

8、or(LMSE)areusedtooptimizetheparametersinthemodellingwhichthenwouldbepluggedintothemodelformula.Atlast,thecontinuouswaveletIII万方数据武汉工程大学硕士学位论文transformbasedontheadaptivewaveletareevaluatedthefaultconditionsofthegearbox.Theresultsoftheexp