Small-Sample Motor Fault Identification via Fusion of Fixed-Resolution and Multiscale Time–Frequency Features

Motor fault identification is often constrained by scarce labeled samples and the limited representation capability of a single time–frequency transform. Conventional CNN–Softmax models may also produce unstable decision boundaries under small-sample conditions. To address these issues, this paper proposes a motor fault identification method based on the fusion of fixed-resolution and multiscale time–frequency features. Each vibration segment is transformed into short-time Fourier transform (STFT) and synchrosqueezed wavelet transform (SWT) maps. Two parallel convolutional branches extract complementary features, which are fused by element-wise addition and classified using a radial basis function support vector machine. Experiments on the HUST motor multimodal fault dataset show that the proposed method achieves 100% accuracy under the conventional 70%/30% train–test split. When the training proportion is reduced to 20%, 15%, 10%, and 5%, the corresponding accuracies remain at 99.46%, 99.10%, 98.78%, and 96.77%, respectively. Across operating speeds of 5, 10, 20, and 30 Hz, the average accuracies reach 98.75% and 94.61% under the 20% and 5% training conditions. The model also maintains 100% accuracy at signal-to-noise ratios of 15 dB and above. These results demonstrate that complementary time–frequency feature fusion combined with maximum-margin classification improves identification accuracy and decision-boundary stability under limited training data.