This paper's main purpose is to present a fault detection and isolation approach in the circuits employing a convolutional neural network and spectrograms. Monte Carlo analysis is performed for each of the existing faults, and several sample signals are generated. Then, spectrograms for one-dimensional output signals are calculated by the short-time discrete Fourier transform, and the convolutional neural network is trained using the spectrograms. The contribution of this paper is twofold. First, we suggest the power spectrogram to generate the features and apply them to the convolutional neural network. Second, usually, more than one fault occurs in circuit elements. So we study the simultaneous faults, which are the most challenging faults to be detected and isolated. Simulation results show that the proposed method has better accuracy than the existing methods from literature, and the computational time and the rate of false alarms have also reduced.

本文的主要目的是提出一种使用卷积神经网络和频谱图的电路中的故障检测和隔离方法。对每个现有故障执行蒙特卡洛分析，并生成几个样本信号。然后，通过短时离散傅立叶变换计算一维输出信号的频谱图，并使用频谱图训练卷积神经网络。本文的贡献是双重的。首先，我们建议使用功率谱图来生成特征并将其应用于卷积神经网络。其次，通常在电路元件中会发生多个故障。因此，我们研究同时发生的故障，这是要检测和隔离的最具挑战性的故障。