基于小波包去噪和EMD的混合算法

Hybrid algorithm based on wavelet packet denoising and EMD

经验模态分解(EMD)是希尔伯特黄变换(HHT)中的关键步骤,并伴有过冲和端点效应的产生。利用遗传算法(GA)对支持向量机(SVM)中的未知参数:惩罚函数C和高斯核函数中的预设参数σ进行优化选取,运用GA-SVM对信号进行端点延拓来处理端点效应问题并提出采用分段三次Hermite多项式插值进行包络线拟合;为了机械设备早期故障频率的特征提取,采用小波包降噪预处理,结合改进的Hilbert Huang变换进行轴承故障特征频率的提取实验;实验表明该方法提高了故障频率提取的准确性。

Empirical Mode Decomposition（EMD） decomposition is a critical step in Hilbert-Huang Transform（HHT）, accompanied by overshoot and endpoint effect. The Genetic Algorithm（GA） is used to optimize and select the unknown parameters including the penalty function C and default parameters σ of Gaussian kernel of Support Vector Machines（SVM）. GA-SVM is applied to extend signals to deal with endpoint effect, and cubic Hermite polynomial interpolation is adopted for envelope fitting. In order to extract the early stage fault frequency features of mechanical equipment, wavelet packet noise reduction pretreatment is performed, combined with the extraction experiment of bearing fault feature frequency by using improved HHT transform. The experimental results show that the proposed method can improve the accuracy of fault frequency extraction.