A Bearing Fault Diagnosis Method Based on Weighted Differential Time-Frequency Features and a Dual-Branch Interactive Fusion Network.

Existing methods often have difficulty fully characterizing fault information using a single input feature, and the interaction and fusion between local fault impulse features and contextual relationships remain insufficient, which limits diagnostic performance under variable operating conditions and few-shot settings. To address these issues, this article proposes a bearing fault diagnosis method based on weighted differential time-frequency features (WDF) and a dual-branch interactive fusion network. First, first-order differencing is used to enhance local transient information, and the fast Fourier transform (FFT) is employed to extract frequency-domain features. The two feature streams are then weighted and stacked to form WDF as the model input. Then, a multi-scale wide-kernel deep convolutional neural network (MS-WDCNN) branch is designed to extract multi-scale local fault impulse features, while a Swin Transformer branch is introduced to model contextual relationships in two-dimensional feature representations. Subsequently, a Feature Interaction Module (FIM) is proposed to achieve bidirectional interaction and complementary fusion between the two branches, thereby enhancing the discriminability of fault features. Experiments on two public bearing datasets demonstrated that the proposed method achieved average accuracies of 99.45% and 96.12% across eight CWRU tasks and six HUST tasks under variable operating conditions, respectively. In further few-shot experiments, the number of training samples per class was set to 5, 7, 10, 15, and 20, while the test set remained unchanged. Under the most challenging five-shot setting, the proposed method achieved average accuracies of 89.61% and 74.73% on the CWRU and HUST tasks, respectively, further demonstrating its effectiveness under variable operating conditions and few-shot settings.