Rolling bearing fault diagnosis based on data-level and feature-level information fusion

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.

基于超磁致伸缩材料和压电材料的机械放大机构(英文)

超磁致伸缩材料和压电材料在商业产品和军事领域中被广泛应用。尽管这些材料具有输出力大、频响高等优点,其应用依然受制于其输出位移小的问题。使用机械放大机构是解决这一问题的有效途径。研究主要的3种基于超磁致伸缩材料及压电材料的机械放大机构,得出铰链式位移放大机构较适用于压电材料,液压位移放大机构适用于微机电系统而压曲放大机构适用于超磁致伸缩材料的结论。

基于多基站的双臂机器人组合运动性能测量方法

目前,对于出厂或者使用环境发生重大变化的机器人一般使用国标GB/T12642-2013《工业机器人性能规范与试验方法》进行性能测量及准确度保证。该性能规范主要针对单臂机器人规定了判定工业机器人性能等级的14项参数指标及其对应的试验方法,比如位姿准确度和位姿重复性、距离准确度和距离重复性、轨迹准确度和轨迹重复性、位置稳定时间等。