中文摘要：

为了解决特种车辆变速箱圆柱滚子轴承由于振动信号的非线性、非平稳特征较为微弱,提取的特征量数值不明显且现实中难以获得大量含丰富特征的典型故障样本而难以对其进行准确诊断的问题,应用小波包近似熵和支持向量机对特种车辆变速箱圆柱滚子轴承进行诊断。首先,在自行搭建的模拟实验台上采集某型特种车辆变速箱圆柱滚子轴承正常、外圈磨损、滚动体故障、点蚀和压痕4种典型状态的振动信号;然后,分别提取4种典型状态振动信号的小波包近似熵值作为支持向量机的输入,根据支持向量机的输出结果来确定圆柱滚子轴承是否发生故障和故障类型。结果表明,该方法能有效对某型特种车辆变速箱圆柱滚子轴承的典型状态进行诊断,为其他相似变速箱圆柱滚子轴承的故障诊断提供一种参考途径,具有一定的工程实用价值。

英文摘要：

The vibration signals of cylindrical roller bearings in the gearbox of special vehicles are usually weak,nonlinear and non-stationary.Moreover,the extracted feature quantity value is not obvious,and it is difficult to obtain a large number of typical fault samples.These problems make it difficult for roller bearings to be accurately diagnosed.In order to solve those problems,approximate wavelet packet entropy and a support vector machine are used to diagnose these roller bearings.First,four typical state vibrationsignals of the roller bearings are collected,including normal,outer ring wear,rolling element failure,pitting and indentation from the self-built test bench.Second,the approximate wavelet packet entropy value of four typical state vibration signals can be extracted as the input of support vector machines.The results of the support vector machine output can help determine whether the bearing is faulty or the fault type.The results show that this can effectively diagnose the typical state of cylindrical roller bearings in the gearbox of special vehicles,and provide a practical reference for the fault diagnosis of similar gearbox cylindrical roller bearings.