基于小波包变换和GA-PLS算法的故障特征选择方法

Fault Feature Selection Method Based on Wavelet and GA-PLS Algorithm

为了提高故障诊断的分类精度,减小分类运算时间等问题,需要从原始特征集合中选择出更为优化的特征子集合,因此,提出了一种基于小波包变换和GA-PLS算法的特征选择方法。首先,采用小波包变换对提取出的振动信号进行分解,从而得到小波包的分解系数;其次,运用遗传算法-偏最小二乘法从原始信号和小波包系数的统计学特征中选择出最优特征集;最后,将最优特征集作为输入,输入到支持向量机中以实现对不同故障的诊断与识别。应用于轴向柱塞泵故障诊断中,与现有特征选择方法对比,实验结果验证了本研究特征选择方法的有效性。

In order to improve classification accuracy and shorten calculation time in fault diagnosis,the optimal feature set can be selected from the original feature set.Therefore,a feature selection method for the axial piston pump based on wavelet and GA-PLS Algorithm is proposed.Firstly,the vibration signals are decomposed by wavelet transformation,and the decomposition coefficients of wavelet are obtained. Then,the optimal feature set is selected from the original feature set collected from original signal and wavelet coefficients,using the genetic algorithm-partial least square(GA-PLS)algorithm.Finally,the optimal feature set is used as input into a support vector machine(SVM)to diagnose and identify different faults.This feature selection method is applied in the fault diagnosis of axial piston pump.The experimental results verify the effectiveness of this method in comparision with current methods.