An efficient diagnostic strategy for intermittent faults in electronic circuit systems by enhancing and locating local features of faults

Abstract

Abstract Due to their short duration, concealability, and random occurrence, intermittent faults have become the most dangerous hazard in electronic circuit systems. However, existing intelligent diagnostic methods often struggle to provide substantial effectiveness for such objects. This article proposes a scheme to enhance the output signal of electronic circuits to make the features more intuitive and facilitate fault feature mining, and designs a scheme to mine tiny fault features from global signals. First, the circuit’s output signal undergoes an S-transform to obtain its time-frequency domain characteristics. Next, a sequence and excitation networks attention module is employed to allocate weights to different channels. Finally, the aforementioned output is used as input to the Swin transformer framework to thoroughly explore fault features. Three electronic circuits are used as experimental circuits to test the proposed method. The experiment shows that the proposed diagnostic method is fast and has an accuracy of over 97%. Therefore, the effectiveness of the designed strategy that includes multiple attention mechanisms in mining intermittent fault features in electronic circuit systems has been demonstrated.