轴承故障信号识别经常受到各种噪音的影响,传统K奇异值分解(K-Singular value decomposition, K-SVD)算法在稀疏表示中效果较差,通过终止准则对进K-SVD字典学习优化,设计了基于改进K-SVD稀疏表示的轴承微弱故障信号特征处理方法。将终...轴承故障信号识别经常受到各种噪音的影响,传统K奇异值分解(K-Singular value decomposition, K-SVD)算法在稀疏表示中效果较差,通过终止准则对进K-SVD字典学习优化,设计了基于改进K-SVD稀疏表示的轴承微弱故障信号特征处理方法。将终止准则当作字典更新收敛条件,采取正交匹配追踪算法进行稀疏求解,以包络谱形式实施分析,达成对微弱故障特征的提取目标。仿真信号结果表明,添加噪声信号时域图难以对特征频率实施精准提取。通过改进K-SVD算法来学习该分量特征信息有着明显的冲击特征,通过重构误差的波动状况对更新收敛性验证。试验结果结果表明,故障特征频率被其它频率掩盖,导致故障状态难以被有效辨别。本文方法实现对微弱故障特征的高效提取,精准判断故障状态。展开更多
Large eddy simulations generally are used to predict 3D wind field characteristics in complex mountainous areas.Certain simulation boundary conditions,such as the height and length of the computational domain or the c...Large eddy simulations generally are used to predict 3D wind field characteristics in complex mountainous areas.Certain simulation boundary conditions,such as the height and length of the computational domain or the characteristics of inflow turbulence,can significantly impact the quality of predictions.In this study,we examined these boundary conditions within the context of the mountainous terrain around a long-span cable-stayed bridge using a wind tunnel experiment.Various sizes of computational domains and turbulent incoming wind velocities were used in large eddy simulations.The results show that when the height of the computational domain is five times greater than the height of the terrain model,there is minimal influence from the top wall on the wind field characteristics in this complex mountainous area.Expanding the length of the wake region of the computational domain has negligible effects on the wind fields.Turbulence in the inlet boundary reduces the length of the wake region on a leeward hill with a low slope,but has less impact on the mean wind velocity of steep hills.展开更多
An adaptive algorithm operating in the Contourlet domain is presented. Contourlet is a new image sparse representation, which is better than a wavelet for piecewise smooth images with smooth contours. Because of flexi...An adaptive algorithm operating in the Contourlet domain is presented. Contourlet is a new image sparse representation, which is better than a wavelet for piecewise smooth images with smooth contours. Because of flexible multiresolution, local and directional sensitivity of Contourlet transform, our approach also defines significant-tree in the Contourlet domain. By analyzing the relation of the Contourlet coefficients, we embed the watermarking into all the coefficients of each significant-tree. Then referring to the statistical properties of the coefficients, the masking characteristics of texture are defined for adaptively controlling the embedding strength. Experimental results show that the proposed algorithm is highly robust to various attacks, such as JPEG compression, medium filtering, cropping and rotation. Furthermore, comparisons with a classical method in the wavelet domain prove the validity of the new algorithm.展开更多
文摘轴承故障信号识别经常受到各种噪音的影响,传统K奇异值分解(K-Singular value decomposition, K-SVD)算法在稀疏表示中效果较差,通过终止准则对进K-SVD字典学习优化,设计了基于改进K-SVD稀疏表示的轴承微弱故障信号特征处理方法。将终止准则当作字典更新收敛条件,采取正交匹配追踪算法进行稀疏求解,以包络谱形式实施分析,达成对微弱故障特征的提取目标。仿真信号结果表明,添加噪声信号时域图难以对特征频率实施精准提取。通过改进K-SVD算法来学习该分量特征信息有着明显的冲击特征,通过重构误差的波动状况对更新收敛性验证。试验结果结果表明,故障特征频率被其它频率掩盖,导致故障状态难以被有效辨别。本文方法实现对微弱故障特征的高效提取,精准判断故障状态。
基金supported by the National Natural Science Foundation of China(Nos.51925808 and 52178516)the Natural Science Foundation of Hunan Province(Nos.2020JJ5745 and 2023JJ20073),China.
文摘Large eddy simulations generally are used to predict 3D wind field characteristics in complex mountainous areas.Certain simulation boundary conditions,such as the height and length of the computational domain or the characteristics of inflow turbulence,can significantly impact the quality of predictions.In this study,we examined these boundary conditions within the context of the mountainous terrain around a long-span cable-stayed bridge using a wind tunnel experiment.Various sizes of computational domains and turbulent incoming wind velocities were used in large eddy simulations.The results show that when the height of the computational domain is five times greater than the height of the terrain model,there is minimal influence from the top wall on the wind field characteristics in this complex mountainous area.Expanding the length of the wake region of the computational domain has negligible effects on the wind fields.Turbulence in the inlet boundary reduces the length of the wake region on a leeward hill with a low slope,but has less impact on the mean wind velocity of steep hills.
基金the National High Technology Research and Development Program of China(No.2003AA148040)the National Natural Science Foundation of China(No.10471151,60216263,6990312)
文摘An adaptive algorithm operating in the Contourlet domain is presented. Contourlet is a new image sparse representation, which is better than a wavelet for piecewise smooth images with smooth contours. Because of flexible multiresolution, local and directional sensitivity of Contourlet transform, our approach also defines significant-tree in the Contourlet domain. By analyzing the relation of the Contourlet coefficients, we embed the watermarking into all the coefficients of each significant-tree. Then referring to the statistical properties of the coefficients, the masking characteristics of texture are defined for adaptively controlling the embedding strength. Experimental results show that the proposed algorithm is highly robust to various attacks, such as JPEG compression, medium filtering, cropping and rotation. Furthermore, comparisons with a classical method in the wavelet domain prove the validity of the new algorithm.
