A Fault Diagnosis Model Based on Spatial Reconstruction and Soft Pooling Local Enhancement in Small Samples

In rotating machinery fault diagnosis, the scarcity of fault samples significantly impacts the accuracy in industrial scenarios. This paper proposes a novel fault diagnosis model—spatial reconstruction and soft pooling local enhancement network (SR-SPNet), specifically designed for scenarios with limited sample availability. First, the collected vibration signals are transformed into images using continuous wavelet transform (CWT), thereby enriching feature representation and enabling convolutional neural networks (CNNs) to effectively capture relevant features. Next, a multi-scale dilated convolution (MDCNN) is employed to expand the receptive field. To reduce redundant computation and improve feature representation, a spatial reconstruction module (SRM) is introduced to differentiate key features. An improved residual (IR) block is then utilized to further extract crucial features, mitigating the risk of gradient vanishing during training. Additionally, a soft pooling local enhancement module (SPEM) is introduced to enhance the spatial feature representation of the feature maps. Experimental results on the CWRU public dataset and gearbox fault dataset demonstrate that, under conditions of limited sample size, SR-SPNet outperforms several comparative models, exhibiting superior diagnostic performance.