Intermittent fault diagnosis of analog circuit based on enhanced one-dimensional vision transformer and transfer learning strategy

Abstract

As the major cause of false alarms in built-in test (BIT) system, intermittent faults of analog circuits may trigger abnormal equipment shutdown and lead to catastrophic accidents. With complete randomness and great non-repeatability, intermittent faults are arduous to be detected. To enhance the reliability and safety of electronic systems, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is proposed to realize intelligent diagnosis for intermittent faults of analog circuits. The signal anomaly caused by intermittent faults can be regarded as a kind of random anomaly from global perspective, and there are also rich local feature information in the fault interval. Completely composed of self-attention mechanism, Vision Transformer possesses prominent performance on extracting global features and modelling global representations, thus can be applied to identify intermittent faults. Meanwhile, to further enrich the feature representation, one multi-scale convolution fusion module (MSC) incorporating a series of convolution operations is designed and combined with 1DViT to extract and fuse the valuable local information. However, in practical test, due to the complex operation process, it is cumbersome to collect sufficient fault data to guarantee the effective training of the proposed model. To cope with this problem, transfer learning strategy is introduced. The model will be first pre-trained with adequate simulation data which is easily accessible, and then fine-tuned with a relatively small amount of actual fault data to help match the practical feature distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test.