LSFConvformer : a lightweight method for mechanical fault diagnosis under small samples and variable speeds with time-frequency fusion

Transformer-based models have recently demonstrated notable strengths in intelligent fault diagnosis due to their capability for global feature extraction and effective modeling of long-range dependencies. Despite these advantages, existing Transformer-based fault diagnosis methods still encounter several limitations: First, lightweight Transformers still suffer from feature information loss, leading to inadequate complex feature extraction capability. Second, most Transformer-based fault diagnosis methods primarily focus on feature extraction from time-domain signals, which results in poor utilization of feature information in small sample and variable speed data. This study proposes an LSFConvformer framework to address the above issues. First, a lightweight Convformer module is designed to enhance the capture of complex feature information efficiently while reducing the number of learnable parameters and computational load of the intelligent fault diagnosis model. Second, a Shuffle time-frequency feature fusion module is introduced to enhance the multidimensional characteristics and richness of fault features, improving the diagnostic performance of Transformer on small sample and variable speed data. Experimental results on two small sample and variable speed fault datasets show that the proposed method effectively combines the advantages of lightweight architecture and diagnostic robustness, achieving superior accuracy and generalization performance in intelligent fault diagnosis scenarios.