基于GMKL-SVM的模拟电路故障诊断方法

提出了一种新颖的基于广义多核支持向量机(GMKL-SVM)的模拟电路故障诊断方法。首先,应用Haar小波分析提取被测电路时域响应信号的小波系数作为特征参量,并生成样本数据;然后,基于样本数据,应用量子粒子群算法对GMKL-SVM的参数进行优化,并以此建立基于GMKL-SVM的故障诊断模型,用于区分模拟电路的各个故障。实例电路的单故障和双故障诊断实验结果表明,所提出的GMKL-SVM方法能较好地实现模拟电路故障诊断,与传统的GMKL-SVM方法相比,表现出了更好的性能,获得了更高的故障诊断正确率。

Analog circuit fault diagnosis based on GMKL-SVM method

A novel analog circuit fault diagnosis approach is presented by using generalized multiple kernel learning support vector machine (GMKL SVM) algorithm. Firstly, the wavelet coefficients of measured time responses are generated as features by using a Haar wavelet transform. Then, wavelet features are used as samples to identify parameters for GMKL SVM method with using quantum behaved particle swarm optimization (QPSO) algorithm. As a result, classification model based on GMKL SVM method is constructed to diagnosis analog circuit faults. Dignostics on both single fault and double faults demonstrate that the proposed GMKL SVM method can obtain good diagostic performance on analog circuit fault diagnosis. Additionally, compared to the traditional GMKL SVM method, the presented approach has higher diagnostic precision.