Unexpected failures in rotating machinery can cause costly downtime and safety hazards in industrial systems, highlighting the need for accurate and robust fault diagnosis. However, fault-related signals are often weak and easily obscured by noise, making reliable diagnosis challenging in real-world environments. To address this, we propose Time-frequency and time-series dual-branch fusion network(TFDFNet), a novel dual-branch deep learning model designed to improve fault classification performance under noisy and complex conditions. The model combines two complementary types of information: time-frequency representations derived from continuous wavelet transform and raw time-sequence data captured through sliding-window sampling. A Swin Transformer is used to extract deep features from time-frequency images, while a specially designed module called Gated attention block(GABlock) learns key temporal patterns from the sequence data. These features are fused using a cross-attention mechanism to enhance fault-related information. Extensive experiments on two public bearing fault datasets (CWRU and Ottawa) show that TFDFNet achieves outstanding accuracy, even under severe noise interference. The model reaches up to 100% accuracy on CWRU and 99.44% on Ottawa, and consistently outperforms existing convolutional neural network (CNN) baselines. These results demonstrate the practical potential and robustness of TFDFNet for intelligent fault diagnosis in industrial applications.
Tang et al. (Thu,) studied this question.