提出一种基于高阶统计量特征提取的径向基函数网络齿轮故障分类方法。以齿轮箱振动信号的高阶统计量估计值作为齿轮故障特征，以径向基函数神经网络作为分类器，成功地对齿轮故障进行了分类。研究表明，高阶统计量和径向基函数神经网络相结合的齿轮故障分类方法是有效的 A gear fault classification method for radial basis function network based on high-order statistic feature extraction is proposed. Taking high-order statistic estimates of gearbox vibration signals as the fault characteristics of gears and radial basis function neural networks as classifiers, gear faults were successfully classified. The research shows that the method of gear fault classification based on high-order statistics and radial basis function neural network is effective