在基于频率响应的非线性模拟电路故障诊断中, 为了克服现有方法的不足, 更好地选择测试激励信号, 利用Volterra级数进行建模, 提出了频率基搜索和遗传算法相结合的测试激励智能优化设计方法。先通过全局搜索得到使各阶核输出频率不重合的频点集, 再以各故障状态特征向量之间的欧氏距离之和为目标函数对频率基进行筛选, 得到最优测试激励信号, 并以一非线性电路验证了该方法的有效性。

In order to overcome the shortcomings of the current methods in the nonlinear analog circuit fault diagnosis based on frequency response, and select a better test stimulus signal, we used Volterra series for modeling, and proposed a method for intelligent optimization of test stimulus by using the frequency basis search together with the genetic algorithm.Firstly, the comprehensive searching technique was adopted to search the best frequency point sets, which make the output frequency point sets of each kernel not coincident with each other.Then, the frequency basis was filtered by taking the sum of the Euclidean distances between the characteristic vectors of the fault status as the objective function.Thereby the optimal test excitation signal was obtained.A nonlinear circuit was used for verification.