A Multi-Modal Contrastive Learning Framework for Intelligent Fault Diagnosis with Enhanced Time-Frequency Alignment

In the field of bearing fault diagnosis, contrastive learning has shown significant effectiveness. However, existing frameworks predominantly focus on a single information domain and modality, limiting their ability to capture fault-related information. To address this issue, this paper introduces wavelet graphs as inputs in the frequency domain and proposes a Multi-Modal Contrastive Learning (MCL) framework to extract potential fault features from unlabeled data. Specifically, this framework includes a time encoder designed to capture representations of temporal signals and their augmentations, along with a frequency encoder that extracts representations of wavelet graphs to obtain richer fault features. By employing triplet loss for pre-training, this approach maximizes the inter-modal correlation of features between the frequency and temporal domains, as well as the correlation of features within the temporal domain. We evaluate the efficacy of our proposed method on the Case Western Reserve University dataset. Experimental results demonstrate that our approach surpasses state-of-the-art research studies, particularly under limited annotations.