基于稀疏分类算法的矿物传送设备故障诊断方法

Fault diagnosis method of mineral transmission equipment based on sparse classification algorithm

针对现有基于特征频率识别的矿物传送设备故障诊断方法存在易受强噪声干扰的问题,提出了基于稀疏分类算法的矿物传送设备故障诊断方法。首先,利用计算机测取设备已知故障类型的振动信号,并对其进行傅里叶变换;然后,以傅里叶变换系数构造训练字典,将待测故障类型的振动信号傅里叶变换系数在该训练字典上进行稀疏分解,求取稀疏系数;最后,利用重构信号最小误差判别故障类型。仿真和测试结果表明,该方法能有效诊断出矿物传送设备中轴承的故障类型,为煤矿传送设备的故障监测提供了一种新方法。

In view of the problem that the existing fault diagnosis methods based on feature frequency identification for mineral transmission equipments are susceptible to strong noise,a new fault diagnosis method based on sparse classification algorithm for mineral transmission equipment was proposed.Firstly,vibration signals for the known fault types of equipment are collected by computer and transformed by Fourier transformation.Then,the Fourier transformation coefficient vectors of test vibration signal are sparsely coded on a dictionary,which is constructed by merging the Fourier transformation coefficientvectors of the known vibration signals,so as to get sparse coefficient.At last,the fault types of the test samples are labeled by identifying their minimal reconstruction errors.The simulation and test results demonstrate that the method can effectively diagnose the fault type of bearing of mineral transmission equipment,which provides a novel method for fault monitoring of transmission equipment in coal mine.