基于模糊计算法的皮带机信号辨识

Signal Identification of Belt Conveyor Based on Fuzzy Computing Method

为了使出料皮带机简化为悬臂梁后的激励力更贴近于合理值,先获得悬臂梁被激励时力传感器采集到的数据集,再运用模糊贴近方法计算最贴近试验数据的观测模糊向量。然后,为了确定试验信号中较大幅值的分布情况,对6个信号幅值运用模糊聚类算法进行迭代计算其较大幅值的聚类中心。最后,运用分类数据的较大幅值作为代表值,将含有的关键数据替代原信号幅值。分析表明移动破碎装置中出料皮带机的加速度振幅值能够较好地聚类,为技术人员提供了重要依据。

In order to make the excitation force of the belt conveyor simplified as a cantilever beam closer to a reasonable value,the dataset collected by the force sensor when the cantilever beam is excited is first obtained,and then the observation fuzzy vector closest to the experimental data is calculated using the fuzzy approximation method.Then,in order to determine the distribution of larger values in the experimental signal,a fuzzy clustering algorithm was used to iteratively calculate the clustering centers of the larger amplitudes of the six signal amplitudes.Finally,the larger amplitude of the classified data is used as a representative value to replace the original signal amplitude with the key data contained.The analysis shows that the acceleration amplitude values of the discharge belt conveyor in the mobile crushing device can be well clustered,providing important basis for technical personnel.