This article proposed a practical approach to isolating faults in analog circuits. The contribution of this article is twofold. First, the optimized empirical mode decomposition approach is presented based on the Hellinger distance such that there is a minimum dependency between intrinsic mode functions. Features with high distinction could be extracted by employing intrinsic mode functions in fault detection problem of analog benchmark circuits. Second, the non-dominated sorting genetic algorithm is employed to retain excellent features and speed up the execution, resulting in the high accuracy of fault detection and isolation. The number of features and mean squared error are selected as objective functions. The features from the data are also extracted using the fast Fourier and wavelet transforms for comparison. Finally, the support vector machine and artificial neural network are employed to isolate faults. Two circuits under test are simulated, and the output signals of the faulty and fault-free circuits are extracted by the Monte Carlo analysis. According to the obtained simulation results, the proposed method with a low-dimensional feature vector outperformed the previous methods, and the computational time has also reduced significantly.

本文提出了一种隔离模拟电路故障的实用方法。这篇文章的贡献是双重的。首先，基于 Hellinger 距离提出了优化的经验模式分解方法，使得固有模式函数之间的依赖性最小。在模拟基准电路的故障检测问题中，可以通过使用固有模式函数来提取具有高区分度的特征。其次，采用非支配排序遗传算法，保留优良特征，加快执行速度，实现故障检测和隔离的高准确率。选择特征数和均方误差作为目标函数。还使用快速傅立叶变换和小波变换从数据中提取特征以进行比较。最后，采用支持向量机和人工神经网络进行故障隔离。对两个被测电路进行仿真，通过蒙特卡罗分析提取有故障和无故障电路的输出信号。根据得到的仿真结果，所提出的具有低维特征向量的方法优于以前的方法，并且计算时间也显着减少。