Gear pump is the key component in hydraulic drive system, and it is very significant to fault diagnosis for gear pump. The combination of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine is proposed for fault diagnosis of gear pump in this article. Sparsity empirical wavelet transform is used to obtain the features of the vibrational signal of gear pump, the sparsity function is potential to make empirical wavelet transform adaptive, and adaptive dynamic least squares support vector machine is used to recognize the state of gear pump. The experimental results show that the diagnosis accuracies of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine are better than those of the empirical wavelet transform and adaptive dynamic least squares support vector machine method or the empirical wavelet transform and least squares support vector machine method.

齿轮泵是液压驱动系统中的关键部件，对齿轮泵的故障诊断具有十分重要的意义。提出了将稀疏经验小波变换与自适应动态最小二乘支持向量机相结合的齿轮泵故障诊断方法。利用稀疏经验小波变换获得齿轮泵振动信号的特征，稀疏函数具有使经验小波变换自适应的潜力，并采用自适应动态最小二乘支持向量机识别齿轮泵的状态。