A novel hybrid-attention and residual mechanism–based transfer network for diesel engine small-sample misfire fault diagnosis under noise conditions

Abstract Recently, significant progress has been made in the research on the diagnosis of diesel engine faults. However, the detection of diesel engine misfire faults under noise conditions often faces the issue of limited available samples. This paper proposes a novel hybrid-attention and residual mechanism–based transfer network (HARTN) for small-sample misfire fault diagnosis under noise conditions. In the feature extraction module, residual blocks and the introduced channel attention and spatial attention mechanism together create a dual-path feature extractor. The hybrid attention mechanism can adjust the channel weights and position parameters, and the original rich information can be preserved through residual blocks. For the classification module, specially designed diverse convolution kernel sizes and layers achieve a fusion of multiscale features before classification. With a target domain sample size of 180 for fine-tuning the model, the average accuracy of HARTN was 99.446% under −10 dB, whereas that of CNN, ResNet, RTN, HATN, and HARN_DNN was 94.727%, 81.985%, 97.526%, 75.898%, and 95.565%, respectively. For variable sample diagnosis tasks, HARTN achieved an accuracy of over 97% with a target domain sample size of only 10, which was better than compared models.