为提升柱塞泵振动数据采集质量、故障诊断准确率和诊断模型的鲁棒性,对采集柱塞泵振动数据时,确定传感器安装位置的方法展开研究,分析了传感器的安装位置对柱塞泵振动数据质量的影响,给出了传感器安装位置评价方法。利用AMESim构建了力...为提升柱塞泵振动数据采集质量、故障诊断准确率和诊断模型的鲁棒性,对采集柱塞泵振动数据时,确定传感器安装位置的方法展开研究,分析了传感器的安装位置对柱塞泵振动数据质量的影响,给出了传感器安装位置评价方法。利用AMESim构建了力士乐A11VO系列9柱塞泵仿真模型,通过研究传感器各项性能指标、柱塞泵结构及运动机理,分析得到柱塞泵振动数据采集实验中合理的传感器安装位置。提出基于平方包络的基尼指数(Gini index of square envelope,GISE)柱塞泵故障振动信号质量评价方法,并设计柱塞泵振动数据采集实验进行验证。得到GISE等4种稀疏性指标的指示结果与柱塞泵结构分析结果一致,分析并证明了GISE指标在柱塞泵信号评估上的有效性与优越性。展开更多
A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-...A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.展开更多
文摘为提升柱塞泵振动数据采集质量、故障诊断准确率和诊断模型的鲁棒性,对采集柱塞泵振动数据时,确定传感器安装位置的方法展开研究,分析了传感器的安装位置对柱塞泵振动数据质量的影响,给出了传感器安装位置评价方法。利用AMESim构建了力士乐A11VO系列9柱塞泵仿真模型,通过研究传感器各项性能指标、柱塞泵结构及运动机理,分析得到柱塞泵振动数据采集实验中合理的传感器安装位置。提出基于平方包络的基尼指数(Gini index of square envelope,GISE)柱塞泵故障振动信号质量评价方法,并设计柱塞泵振动数据采集实验进行验证。得到GISE等4种稀疏性指标的指示结果与柱塞泵结构分析结果一致,分析并证明了GISE指标在柱塞泵信号评估上的有效性与优越性。
基金supported in part by the National Natural Science Foundation of China under Grants No.62027803,No.61601096,No.61971111,No.61801089,and No.61701095in part by the Science and Technology Program under Grants No.8091C24,No.80904020405,No.2021JCJQJJ0949,and No.2022JCJQJJ0784in part by Industrial Technology Development Program under Grant No.2020110C041.
文摘A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.
文摘利用滑坡灾害普查资料和气象资料,结合地理信息系统(GIS)和降雨推算模型进行空间分析,对庆元县滑坡与降雨作相关研究后发现:降雨具有诱导和直接触发滑坡的综合作用效果;庆元县滑坡的时空分布,受降雨地区和降雨时间的控制,并与一定的地质条件及人类活动有关;滑坡剧烈活动时间与降雨时间及暴雨、大暴雨频次吻合或略滞后,庆元县滑坡的起动降雨量为:日降雨量≥50 mm或滑坡前10 d累积降雨量≥100 mm.
