Mechanical fault diagnosis of small sample based on frequency-guided multiscale CNN model

Abstract The study of advanced rotating machinery fault diagnosis technology is of great significance to improve the safety and reliability of equipment operation. Training samples are difficult to obtain in engineering practice, leading to weak generalization and low diagnostic accuracy of deep learning-based fault diagnosis methods. To solve the problem of insufficient small sample data to support the training of traditional intelligent diagnostic methods, a small sample fault diagnosis method based on frequency-guided multi-scale convolutional neural network (CNN) model is proposed in this paper. The model consists of multi-scale feature extraction (MFE), dual convolution fusion (DCF), frequency guided feature fusion (FGFF) module, and kolmogorov–arnold networks fully connected (KAN-FC) module. The MFE can comprehensively extract original fault signal features even with small sample data; the DCF effectively integrates the attention weight relationships between channels and spatial dimensions; and the FGFF integrates features extracted from multiple branches to build the feature network relationship. Additionally, the KAN is introduced as the fully connected layer of the model, which can better adapt to the differences in small data samples under various working conditions. When the proportion of the small sample training set is 0.3, the G-mean value on the Case Western Reserve University dataset is 99.89%, and the G-mean value on the Paderborn University (PU) dataset is 100%, which is approximately 0.59%–9.69% higher than that of other models. Through comparative verification, it is demonstrated that the proposed model outperforms existing models in small sample fault diagnosis and has strong generalization performance.