A multi-level constrained vibration–acoustic multimodal contrastive learning for cross-machine motor fault diagnosis
A novel vibration–acoustic multimodal contrastive learning framework designed to jointly regularize vibration–acoustic features at the levels of sample distribution, feature statistics, and feature structure enhances multi modal consistency, feature discriminability, and information diversity.