基于DCQGA-SMKL-SVM的模拟电路故障诊断方法

提出了双链量子遗传算法(DCQGA)优化简单多核支持向量机(SMKL SVM)的模拟电路故障诊断方法。首先，提取测试电路时域响应信号，用Harr小波对响应信号进行变换并归一化处理，得到特征参数;其次，用双链量子遗传算法优化SMKL SVM的参数，以此建立起DCQGA SMKL SVM故障诊断模型，用于模拟电路故障诊断。双二次滤波器电路与四运放二阶高通滤波器电路作为仿真测试电路，仿真测试结果表明，提出的故障诊断方法实现了模拟电路故障诊断，相比于DCQGA SVM模拟电路故障诊断方法，诊断正确率更高。

An analog circuit fault diagnosis methodbased on DCQGA-SMKL-SVM

We present an analog circuit fault diagnosis approach by using the double chain quantum genetic algorithm to optimize the simple multi-kernel learning support vector machine (DCQGA-SMKL-SVM). Firstly, we extract the time domain response signals of the test circuit, and utilize the Harr wavelet to process and normalize response signals to obtain characteristic parameters. Then, the parameters of the SMKL-SVM are optimized by the DCQGA and an analog circuit fault diagnosis model based on the DCQGA-SMKL-SVM method is constructed for analog circuit fault diagnosis. Tests on the bi-quadratic filter circuit and quad-amplifier second-order high-pass filter circuit show that compared with the DCQGA-SVM method, the proposed approach has higher fault diagnosis accuracy.