Intelligent diagnosis method for compound faults in wheelset–axlebox systems of urban rail vehicles based on the fusion of vibration signals and infrared thermal images

To address the limited representation of compound fault features and the susceptibility of single-modal diagnosis to noise in wheelset–axlebox systems of urban rail vehicles, this study proposes a multimodal diagnostic method integrating vibration signals and infrared thermal images. A wheelset–axlebox fault simulation platform was established to acquire vibration and infrared data in parallel under different equivalent vehicle-speed conditions, followed by temporal pairing to construct a multi-condition dataset containing normal, single-fault, and compound-fault conditions. Vibration signals were transformed into short-time Fourier transform (STFT) time–frequency maps and Gramian angular field (GAF) temporal structure maps and combined with infrared thermal images as three-modal inputs. A Tri-Modal Attention-Enhanced Residual Network (TAER-Net) was developed with three residual branches and a proposed Improved Convolutional Block Attention Module (ICBAM) for feature enhancement and feature-level fusion. The method achieved high classification performance for the tested fault conditions across four speed datasets, with a maximum accuracy of 98.4%. Ablation, comparative, and noise-robustness experiments further demonstrated the effectiveness of the multimodal fusion framework and TAER-Net under the tested conditions. The results provide a methodological reference for multimodal intelligent diagnosis of wheelset–axlebox faults using complementary vibration and thermal-spatial information.