Fault Diagnosis of Analog Circuit Based on Spatial-Temporal Feature Attention Network

Intelligent fault diagnosis methods have achieved significant results in airborne electronic systems. However, due to the randomness and non-repeatability of the fault mode, most available intelligent fault diagnosis methods are not very effective. Meanwhile, the current approach focuses equally on different features. To solve the above problems, a diagnosis model based on a spatial-temporal feature attention network (STFAN) is proposed. Firstly, a multiple convolutional layer is designed and an efficient channel attention-Residual neural network (ECA-ResNet) module is applied to enhance the convolutional channel information. This design enhances the model's efficient extraction of key features by adjusting the different concerns of different features. Secondly, the data containing spatial features are fed into a bidirectional long short term memory (BiLSTM) network, which considers both past and future information and can capture long-term dependent features of the sequence data. Additionally, a fault injection method is proposed. This method can simulate the different working states of the circuit elements and ensure the randomness of the fault, which effectively addresses the issue of limited fault data. Finally, experiments on two circuits show that the proposed STFAN model obtains an average accuracy of 97.66%. Comparisons with other intelligent diagnostic models show the superiority of the proposed method in accuracy and stability.