Simultaneous fault localization and detection of analog circuits using deep learning approach. (June 2021) - Moezi, Alireza; Kargar, Seyed Mohamad
Highlights: We convert the generated signals to two-dimensional images and then apply them to the convolutional neural network for classification. The simultaneous faults are studied, which is most challenging to detect and isolate. The proposed method outperforms better than other previous methods in terms of speed and accuracy. Abstract: 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. Graphical abstract: Image, graphical abstract