基于小波包分析和相关向量机的电路故障诊断

Circuit Fault Diagnosis Based on Wavelet Packet Analysis and Relevance Vector Machine

针对模拟电路故障变化的复杂性,提出一种小波包分析和相关向量机的电路故障诊断模型,首先采集模拟电路不同故障状态下的输出信号,将输出信号进行小波包分解,提取分解信号的归一化能量特征,然后将特征向量输入相关向量机中进行训练,建立模拟电路故障诊断模型,实现不同的故障状态分类识别;最后通过仿真实例对模型性能进行测试.测试结果表明,相对于其他模拟电路故障诊断模型,该模型不但提高了模拟电路故障诊断的正确率,而且减少了故障诊断时间.

In order to improve the fault diagnosis accuracy of analog circuit,the authors proposed an analog circuit fault diagnosis model based on wavelet packet analysis and relevance vector machine.Firstly,different fault output signals of analog circuit were collected and decomposed by wavelet packet to extract normalized energy features of signal,and then the feature vectors were input to relevance vector machine to train and establish analog circuit fault diagnosis model to realize the classification and identification,and finally the simulation example was used to test the performance.The results show that compared with other analog circuit fault diagnosis models,the proposed model not only improves the fault diagnosis accuracy rate but also increase the fault diagnosis speed of analog circuit.