A multi-level constrained vibration–acoustic multimodal contrastive learning for cross-machine motor fault diagnosis
TL;DR
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.
Abstract
In order to address common challenges in industrial environments, including limited labeled samples, variable operating conditions, and cross-machine fault diagnosis, this study proposes a novel vibration–acoustic multimodal contrastive learning framework. First, a multi-level constrained contrastive learning method is designed to jointly regularize vibration–acoustic features at the levels of sample distribution, feature statistics, and feature structure. This enhances multi modal consistency, feature discriminability, and information diversity, enabling the learning of robust joint representations. Second, a dynamic frequency domain collaborative enhancement module is introduced during the fine-tuning stage, which progressively integrates frequency domain features into vibration time series features while adaptively adjusting feature weights, thereby improving discriminative capability and representation stability. Finally, the proposed framework is validated on two motors with significant differences in structure and operating conditions, as well as on a series of variable condition and cross-machine fault diagnosis tasks. Experimental results demonstrate that, with only 10 labeled samples per class, the method achieves over 99.57% diagnostic accuracy in cross-machine tasks, highlighting its adaptability to distribution shifts and structural differences and confirming its robust generalization performance in cross-machine and cross condition fault diagnosis.