Intermittent fault diagnosis for electronics-rich analog circuit systems based on multi-scale enhanced convolution transformer network with novel token fusion strategy

Intermittent faults of analog circuits in the core area of electronic systems may induce false alarms of built-in test (BIT) system and even cause catastrophic accidents. Due to notable non-repeatability and high randomness, intermittent faults are arduous to be detected. In order to enhance the safety and reliability of electronic systems, an end-to-end diagnostic framework based on multi-scale enhanced convolution Transformer network (MSECTN) with novel token mixing and fusion strategy has been developed. Intermittent fault is a kind of random fault from global perspective, and there is also abundant local information inherent in the fault interval. Wholly composed of self-attention mechanisms, Transformer network possesses prominent capability of representing global features and modelling distant temporal associations, thus can be applied to identify intermittent faults. Meanwhile, introduce the local perception of convolution operation into Transformer network. One convolution embedding and convolutional multi-head self-attention module are designed to capture more abundant local details. Since the information at single scale cannot sufficiently reflect the circuit status, multi-scale convolution embedding with kernels in different sizes is performed to enrich the feature representation. Moreover, one token mixing strategy incorporating Fourier transformation, pooling operation, and depth-wise convolution is proposed to simultaneously model the frequency correlation and enhance the information interaction among spatially adjacent tokens. In order to excavate more inherent and distinguishing features, a novel token fusion strategy is designed to eliminate the effects of redundant feature information. Experimental results on two typical analog circuits demonstrated that the proposed approach can effectively identify different intermittent faults.