基于SSA-VMD和ANFIS的黏弹夹层结构老化状态智能识别方法

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关键词：黏弹夹层结构；变分模态分解；特征提取；自适应神经模糊推理系统；老化状态智能识别 中图分类号：TH17 文献标志码：A DOI：10.16385/j.cnki.issn.1004-4523.202308028

Intelligent identification method for aging state of viscoelastic sandwichstructurebasedonSSA-VMDandANFIS

QU Jinxiu 1 ， SHI Xiaowei¹，SHI Changquan²，HUANG Jiaqi，BAI Yumei¹， WU Jiayan¹，KE Fei¹，CAO Wei1 （1.School of Mechatronic Engineering，Xi'an Technological University，Xi'an 71Oo21，China; 2.State Key Laboratory for Manufacturing System Engineering，Xi'an Jiaotong University，Xi'an 71Oo49，China）

Abstract：Aimingatthedificulties thatthevibrationresponsesignaloftheviscoelasticsandwichstructureisstronglynonstation aryandthechangeofvibratioresponsesignalcausedbythechangeofagingstateisweak，thispaperproposesanintellgeatidenti ficationmethodfortheagingstateoftheviscoelasticsandwichstructurebasedonsparowsearchalgorithm（SSA）optimizedvariational mode decomposition（VMD）andadaptiveneuro-fuzzinferencesystem（ANFIS）.Thevibrationresponsesignalsofdifer entagingstatesof the viscoelasticsandwich structureare decomposed bythe parameteroptimizedVMD，andseveral intrinsic modefunctions（IMFs）areobtained；Thepermutation entropy（PE）featuresoftheobtained IMFcomponentsare computed， whichareused toreflectthestructuralagingstatechange；Theobtained permutationentropyfeaturesareconstructedintofeature vectorsasinputsofANFIStorealizetheagingstateinteligenticlentificationofviscoelasticsandwichstructure.Theeffectiveness of the methodwas verfied through experiments，andcompared withempiricalmodedecomposition（EMD）andANFIS，parame teroptimized VMDandradialbasis function neural network（RBFNN）methods.Theresultsshow that theproposed method in this paper can more accurately identify the aging state of viscoelastic sandwich structure.

Keywords：viscoelasticsandwichstructure；variationalmodedecomposition；featureextraction；adaptiveneurofuzzyinference system;intelligent recognition of aging state

黏弹夹层结构是一种将黏弹性材料通过预紧力约束在弹性面板之间的特殊结构。（剩余16417字）