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首页> 外文期刊>Journal of Systems and Control Engineering >Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization
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Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

机译：基于多目标优化的优化集合经验分解方法模拟电路的故障隔离

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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.
机译：本文提出了一种实用的方法来隔离模拟电路故障。这篇文章的贡献是双重的。首先，基于Hellinger距离呈现优化的经验模式分解方法，使得内部模式功能之间存在最小依赖性。通过在模拟基准电路故障检测问题中采用内在模式功能，可以提取具有高区别的功能。其次，采用非统治分类遗传算法来保留优异的特征并加快执行，从而高精度检测和隔离。选择特征和均方误差的数量作为目标函数。还使用快速傅立叶和小波变换来提取数据的特征以进行比较。最后，使用支持向量机和人工神经网络来隔离故障。模拟两个正在测试的电路，并且通过蒙特卡罗分析提取故障和无故障电路的输出信号。根据所获得的仿真结果，具有低维特征向量的所提出的方法优于先前的方法，并且计算时间也显着降低。

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