Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough corr...Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.展开更多
由于航空发动机工作环境复杂,故障数据稀缺,且单一传感器难以全面表征中介轴承状态,导致现有诊断方法准确率较低。为此,提出了一种基于多传感器信息融合(multi-sensor information fusion,MSIF)和二维卷积神经网络(2-dimensional convol...由于航空发动机工作环境复杂,故障数据稀缺,且单一传感器难以全面表征中介轴承状态,导致现有诊断方法准确率较低。为此,提出了一种基于多传感器信息融合(multi-sensor information fusion,MSIF)和二维卷积神经网络(2-dimensional convolutional neural network,2DCNN)的航空发动机中介轴承故障诊断方法。该方法将多个传感器的时域和频域特征融合为一张RGB图像,从而更加全面地表征中介轴承状态。然后,将生成的RGB图像输入2DCNN模型完成故障诊断。在真实航空发动机试验台的轴承故障数据上的测试中,当训练集与测试集比例为1∶9的小样本条件时,部分传感器组合的诊断准确率即可达99%;比例为7∶3时所有传感器组合的准确率均达100%。此外,所提方法的诊断准确率与基础研究相比,至少提高了13%;且超越了进行对比的5种先进方法。结果表明,该方法不仅实现了航空发动机中介轴承故障的快速精准识别,还在小样本条件下展现出了卓越的诊断性能。展开更多
三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技...三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。展开更多
基金Supported by National Natural Science Foundation of China （60874063） and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
基金This project was supported by the National Natural Science Foundation of China (60672139, 60672140)the Excellent Ph.D. Paper Author Foundation of China (200237)the Natural Science Foundation of Shandong (2005ZX01).
文摘Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.
文摘三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。
