Abstract
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.