基于小波包能量谱和M-ary SVM的功率变换器故障诊断

针对功率变换器的故障诊断问题，提出一种基于小波包能量谱和M-ary支持向量机的故障诊断方法。首先，通过小波包分解得到故障信号能量谱特征向量，并结合傅里叶变换分析故障信号主要频率特征点，实现故障特征向量的降维；然后，基于M-ary支持向量机的分类模型诊断出功率变换器多故障模式。实验结果表明，相比于传统的BP神经网络和一对一支持向量机故障诊断方法，本文方法诊断精度高，需要的子分类器数目少，诊断速度快，适用于在线故障诊断。 

Faults Diagnosis of Power Converter Based on Wavelet

Focusing on the issue of power converter fault diagnosis, a method of power converter faults diagnosis based on wavelet packet energy spectrum and M-ary support vector machine(SVM) is proposed. Firstly, the wavelet packet decomposition is adopted to extract the output voltage energy values, and the fast Fourier transform (FFT) is adopted to analyze the frequency feature points, perform the dimension reduction and obtain the fault feature vectors. Then， various fault modes are isolated based on the M-ary SVM diagnostic model． Experiment results show that compared with traditional BP neural network and one-against-one SVM fault diagnosis methods， the proposed method has high diagnostic accuracy and needs far less number of sub-classifiers, and the diagnosis speed is improved a lot. The proposed method is also suitable for online diagnosis. 