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时间:2020-03-25
《非线性自适应滤波在磁致伸缩导波传感器信号处理中的应用.pdf》由会员上传分享,免费在线阅读,更多相关内容在行业资料-天天文库。
1、第23卷第11期传感技术学报Vol_23No.11CHINESEJOURNALOFSENSORSANDACTUATORS2010年11月NOV.2010ApplicationofNonlinearAdaptiveFilterintheSignalProcessingofMagnetostictiveGuidedWaveTransducerWANGFei,Fuzai,XIANGYiqiang,TANGZhifeng。,ZHURan厂1.InstituteofModernManufactureE增ineering,ZhejiangUniversity,Hangzhou310027,Chi
2、na;、l2.DepartmentofCivilEngineeringzijinggangCampus,zangUniversity,Hangzhou310058,China;l3.InstituteofAdvancedDigitalTechnologiesandInstrumentation,ZhejiangUniversity,Hangzhou310027,ChinaAbstract:Themagnetostictiveguidedwavetransducersignalsprocessedbytraditionalfilteralgorithmsarenotsat-isfact
3、oryintermsoflowersignal—to—noiseratioanddistortionoffilteredsignals.Inviewofthoseproblems,anon—linearadaptivefilteralgorithmbasedonwaveletdecompositionisproposed.Onthebasisofextractingreferencenoisebywaveletdecomposition,theneuralnetworksandparticleswarmoptimizationalgorithmareintroducedintothe
4、traditionaladaptivefilter.Anewnon—linearadaptivefilterisrealizedbythepresentedmethod.Usinglongitudi-nalguidedwavemagnetostictivetransducertodetecteasteelbar,thedetectionsignalsareprocessedbythisalgo—rithmasanexample.Resultsshowthisalgorithmcanimprovethesignal—to—noiseratiomoreeffectivelycompare
5、dtothetraditionalapproach,anditavoidstheproblemofthedistortionoffilteredsignals.Itcanbeusedintheactualsignalprocessingofmagnetostictiveguidedwavetransducerduetoitsreal-timeprocessingcharacteristic.Keywords:magnetostictiveguidedwavetransducer;non—linearadaptivefilter;neuralnetworks;particleswarm
6、optimization;waveletdecomposition.EEACC:E1270doi:10.3969/j.issn.1004—1699.2010.11.016非线性自适应滤波在磁致伸缩导波传感器信号处理中的应用木王飞,吕福在,项贻强,唐志峰,竺冉f,1.浙江大学现代制造工程研究所,杭州310027;、I2.浙江大学土木工程系;杭州310058;lI3.浙江大学数字技术及仪器研究所,杭州310027摘要:针对传统滤波算法对磁致伸缩导波传感器信号滤波,存在降噪效果不理想、滤波后信号失真等问题,提出一种基于小波分解的非线性自适应滤波算法。在利用小波分解提取参考噪声的基础上,将神
7、经网络和粒子群优化算法引入传统自适应滤波中,实现了对信号的非线性自适应滤波。以纵向导波磁致伸缩传感器对钢杆的检测信号为例,应用该算法进行滤波处理,结果表明:这种算法能够有效滤除检测信号中的噪声,具有滤波后信号信噪比高、信号不失真、容易实现实时处理等优点,可以实际应用在磁致伸缩导波传感器信号处理中。关键词:磁致伸缩导波传感器;非线性自适应滤波;神经网络;粒子群优化;小波分解中图分类号:TN911.7;TB553文献标识码:A文章编号:1004—1699(20
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