Channel-attention-based time-frequency dual-branch network for bearing fault diagnosis in warehouse logistics sorting machines

To address the difficulty of fully characterizing both local impacts and time-frequency information in bearing vibration signals from automated logistics sorting equipment using a single representation, this study develops a channel-attention-based time-frequency dual-branch fault diagnosis model. Each vibration window is represented both as a normalized one-dimensional time-series input and a two-dimensional continuous wavelet transform (CWT) time-frequency map. A temporal convolutional network (TCN) branch extracts local waveform and temporal variation features, while a multi-scale convolutional neural network (CNN) branch captures time-frequency textures at different receptive fields. Channel attention is introduced separately into the two branches to reweight informative features, and the two representations are concatenated for classification of normal, inner-race, outer-race, and rolling-element conditions. Experiments are conducted using bearing vibration records collected at 1200 r/min, with non-overlapping time windows used to construct training, validation, and held-out evaluation subsets. The proposed model achieves the highest mean accuracy and Macro-F1 among the baselines considered, and component ablation further supports the combined use of the dual-branch architecture, channel attention, and multi-scale feature extraction. The method provides an experimentally supported reference for bearing condition monitoring in warehouse logistics equipment.