摘要
目的提出一种基于奇异值分解(SVD)技术和Hanke矩阵的多重门限奇异值分解方法(MTSVD),对测量源数据进行降噪,使其更接近理论值,减少试验误差对后续计算结果的影响。方法对获得的系统频响函数(FRFs)施加10%的随机误差,之后利用文中提出的MTSVD方法进行降噪处理,并与未经过去噪处理的预测结果以及经奇异值累积法获得的降噪结果进行对比。结果利用MTSVD方法对试验获取的耦合系统频响函数进行处理后,得到的修正值更接近理论值,并且该方法的降噪效果优于现有的奇异值累积法。结论验证结果表明文中提出的MTSVD方法能有效降噪,减少试验测量源数据携带的误差,使其更接近理论值,因此该方法在运输包装领域具有良好的可行性和应用前景。
The work aims to propose a multi-threshold singular value decomposition(MTSVD)method based on singular value decomposition(SVD)and Hanke matrix to reduce the noise of source data,make it closer to the theoretical value and reduce the effect of experiment error on subsequent calculation.First,the obtained frequency-response function(FRF)of the system was added with 10%random error.Then,the MTSVD method proposed in the paper was used to reduce the noise.Finally,the results were compared with the prediction results without noise reduction and the noise reduction results obtained by singular value accumulation.After the MTSVD method was used to process the frequency response function of the coupling system obtained from the experiment,the modified value was closer to the theoretical value,and the noise reduction effect of this method was better than the existing singular value accumulation method.The verification results show that the MTSVD method proposed in the paper can effectively reduce the noise,decrease the error carried by the test measurement source data and make it closer to the theoretical value.Therefore,this method has prominent feasibility and application prospect in transport packaging.
作者
傅苗苗
王军
卢立新
姜咪
FU Miao-miao;WANG Jun;LU Li-xin;JIANG Mi(School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;Key Laboratory of Advanced Food Equipment Manufacturing Technology of Jiangsu Province,Jiangnan University,Wuxi 214122,China;Shantou Dongfeng Printing Co.,Ltd.,Shantou 515064,China)
出处
《包装工程》
CAS
北大核心
2021年第21期176-180,共5页
Packaging Engineering
基金
国家一流学科建设轻工技术与工程(LITE 2018-29)
国家自然科学基金(51205167)
江苏省自然科学基金(BK20151128)。
关键词
逆子结构方法
频响函数
多重门限奇异值分解
inverse sub-structuring method
frequency response functions
multi-threshold SVD
