LAUSR

Automatic fault diagnosis in analog electronic circuits is one of the interesting and important cases for researchers of this field that has gained substantial improvements in recent decades. Fault detection issue could be transferred into a classification problem. In this paper, a new fault detection method is proposed based on deep Convolution Neural Network (CNN). We used the real part of Power Spectrum Density (PSD) of faulty signals as the input images of CNN. The main reason for this is extracting microstructure features among signals by using PSD which result in a better discrimination amongst wide range of faults. Our method is evaluated by two benchmark circuits. The superior performance of our method is proved by simulation results and compared with other state of arts.