Multimodal vibro-acoustic bearing-fault diagnosis method based on channel fusion and the VACF-CNN model
Bearing-fault diagnosis based on single-modal signals is often constrained by incomplete fault information, whereas existing multimodal fusion methods often suffer from feature redundancy and a heavy preprocessing burden. To overcome these limitations, a vibro-acoustic channel fusion convolutional neural network model that employs channel-level fusion within an end-to-end one-dimensional framework, enhanced by wavelet packet decomposition and a squeeze-and-excitation mechanism, is proposed in this study. This method achieves mean diagnostic accuracies of 99.77% and 98.98% on a self-collected dataset and on the public MAFAULDA dataset, respectively. Additional experimental results indicate that the model maintains strong diagnostic performance under additive white Gaussian noise and variable-load conditions.