TFT multimodal feature fusion fault diagnosis method of rolling bearing and its noise resistance

To address weak fault signatures in rolling bearing vibration signals under strong noise conditions, coupled fault mode interference, and limitations of existing methods in modal representation and feature fusion, a deep learning-based time-, frequency-, and time–frequency representation (TFT) multi-modal fusion framework for fault diagnosis with an attention mechanism is proposed. Specifically, three complementary modalities are constructed, including the time-domain, frequency-domain via fast Fourier transform (FFT), and time–frequency-domain via Multi-scale wavelet convolution (MWC), to comprehensively characterize fault-related information. A parallel convolutional neural network–long short-term memory (CNN–LSTM) architecture is employed to extract spatial and temporal features from each modality. Subsequently, a multi-head attention mechanism with learnable modal weights is introduced to adaptively fuse multi-modal features while enhancing discriminative information and suppressing redundant representations. Experimental results on the HUST bearing dataset demonstrate that the proposed framework achieves 100% classification accuracy for both single and compound faults, outperforming all baseline methods. Under varying signal-to-noise ratio (SNR) conditions, the model maintains consistently stable performance, exhibiting strong noise robustness and excellent generalization capability. Furthermore, t-distributed stochastic neighbor embedding (T-SNE) visualization and cross-dataset validation further confirm the effectiveness and separability of the learned feature representations.