融合小波包分解和CSA的电路故障诊断方法

A Circuit Fault Diagnosis Method by Fusing Wavelet Packet Decomposition and CSA

摘要: 模拟电路的基本特性使得模拟电路故障诊断非常困难。针对此问题提出一种融合小波包分解和克隆选择算法(CSA)的模拟电路故障诊断新方案。首先对模拟电路输出的各类故障电压信号进行小波包分解、重构以及频谱分析，获得相应频谱的频带能量作为故障特征样本，包括训练样本和测试样本。然后用克隆选择算法对训练样本进行自学习，得到各类训练样本的最优聚类中心。最后根据测试样本与聚类中心的欧氏距离对故障进行分类，实现电路故障元件定位。实验结果表明该方法有较高的诊断准确率和较短的收敛时间。

Abstract: The basic characteristics of analog circuit make it very difficult to diagnose. A circuit fault diagnosis method by fusing wavelet packet decomposition and CSA is proposed to this problem. Firstly, wavelet packet is introduced to decompose, reconstruct and analyze kinds of fault voltage signals output by analog circuit; the frequency band energy of the corresponding spectrum is obtained as a fault characteristic sample, including training samples and test samples. Then the training samples were studied by using the CSA, and the optimal cluster center was obtained. Finally, the fault is classified according to the Euclidean distance between the test sample and the cluster center, and the fault element localization of the analog circuit is realized. The experimental results show that the method has higher diagnostic accuracy and shorter convergence time.