摘要
为了无损、便捷检测足尺定向刨花板的动态剪切模量、弹性模量,在振动条件下通过改进型自适应噪声的完全集合经验模态分解(ICEEMDAN)和Laplace小波相关滤波法相结合来实现振动信号模态识别。采集足尺定向刨花板振动信号,通过ICEEMDAN分解得到若干固有模态函数(IMF)分量,将正交性指标和相关系数用作判据指标,并融合二次模态分解,筛选出含有足尺定向刨花板模态信息的IMF分量,再通过Laplace小波相关滤波提取足尺定向刨花板的固有频率,将检测结果与静态实验和仿真分析进行验证。结果表明:频率实验与仿真结果误差较小,在0.8%范围内;长度、宽度方向弹性模量动态检测结果与静态结果之间的相关系数分别为0.81282、0.74848,证明两者存在显著线性关系;面内剪切模量动、静态检测结果之间呈显著线性关系,相关性达到0.73385。
In order to non-destructively and rapidly detect the dynamic shear modulus and modulus of elasticity of full-size oriented strand board,an intrinsic modal identification method under the conditions of the vibration method is proposed,which was achieved by a combination of Improved Complete Ensemble Empirical Modal Decomposition of Adaptive Noise(ICEEMDAN)and Laplace wavelet correlation filtering method.First,the foot-size oriented strand board vibration signals were acquired and decomposed by ICEEMDAN to obtain several intrinsic modal functions(IMF)components with different eigen-scales;secondly,the orthogonality index and correlation coefficient were used as the criterion index,and the quadratic modal decomposition was integrated to screen out the IMF components containing modal information of the full-size oriented strand board;finally,the intrinsic frequency of the full-size oriented strand board was extracted by Laplace wavelet correlation filtering,and the detection result was verified by comparing it with the static experiments and simulation analyses.The results showed that the error between the frequency experiment and the simulation results was small,within 0.8%.The correlation coefficients between the dynamic and static results of the elastic modulus in the length and width directions were 0.81282 and 0.74848,respectively,which proved that there was a significant linear relationship between the results.The in-plane shear modulus results from the dynamic and static tests also showed a significant linear relationship,with a correlation of 0.73385.
作者
窦佳明
周卢婧
刘电霆
张厚江
Dou Jiaming;Zhou Lujing;Liu Dianting;Zhang Houjiang(Guangxi Provincial Key Laboratory of Advanced Manufacturing and Automation Technology,Guilin University of Technology,Guilin Guangxi 541006,China;Guangxi Engineering Research Center of Intelligent Rubber Equipment,Guilin University of Technology,Guilin Guangxi 541006,China;Beijing Forestry University,Beijing 100091,China)
出处
《西南林业大学学报（自然科学）》
CAS
北大核心
2024年第6期168-176,共9页
Journal of Southwest Forestry University：Natural Sciences
基金
广西自然科学基金项目(2020GXNSFBA297124,2021GXNSFAA220038)资助
桂林理工大学科研基金项目(GUTQDJJ2018091)资助。
作者简介
第一作者:窦佳明(1998-),男,硕士研究生。研究方向:工程检测技术。Email:2120211134@glut.edu.cn;通信作者:周卢婧(1987-),女,博士,讲师。研究方向:工程检测技术。Email:Zlujing@glut.edu.cn。
