Multi-channel data fusion and lightweight 1D-CNN for compound axle bearing fault diagnosis in bogie systems

Purpose Traditional diagnostic methods are, in most cases, based on single-sensor data; however, this configuration may fail to capture the complex dynamic signatures associated with multi-component degradation. This paper presents a lightweight deep learning architecture for the fusion of multi-modal sensor data, typically vibration and acoustic signals. Design/methodology/approach The proposed model represents a dual-branch one-dimensional convolutional neural network (1D-CNN) designed to simultaneously process six axis vibration signals and dual-channel acoustic emissions. To ensure the model's generalizability under multiple operating conditions, the study evaluates two distinct data-splitting strategies: a standard random split and a more rigorous sample-based split that accounts for specific data acquisition conditions and trial-to-trial variability. Findings Experimental results, validated on the open-source Beijing Jiaotong University - Rail Autonomous Operations (BJTU-RAO) subway bogie dataset, demonstrate that the fusion-based approach presents significant results compared to single-modality baselines. The architecture achieves high classification accuracy for the 12 distinct health conditions, which precisely identify both single and multiple-component defects. Originality/value These results highlight the potential of data fusion integration to provide a stable, scalable and frugal framework for predictive maintenance in railway systems, combining high diagnostic performance with a minimal computational and memory footprint.