Fault Diagnosis of Analog Circuits Based on Multi-Scale 1D Convolutional Neural Network

Analog circuit is an important component of modern electronic systems. However, the soft fault diagnosis of analog circuits is challenging due to their large parameter variability and complex internal structure. So, this paper proposes an automatic fault diagnosis method based on Multi-Scale 1D Convolutional Neural Network (MS-1D-CNN) for analog circuits. Considering that faults may disappear or weaken during the propagation process and cannot be manifested in the output signals, the fault diagnosis model is trained and constructed based on an optimum set of test points with the maximum degree of fault isolation and least test points. Furthermore, because the data at different test points and time periods have different influences on fault diagnosis, a mixed attention mechanism combining both channel and spatial attention is adopted to extract more critical information in the fault diagnosis model, achieving a more accurate soft fault diagnosis for analog circuits. Experiments are conducted on four widely used benchmark circuits. The results show that our method has higher accuracy of fault diagnosis than the existing methods, and the fault diagnosis model based on multi-test points data has higher accuracy than that solely based on the output signals for analog circuits.