Multimodal Correlation-Aware Fusion Framework for Enhanced Machinery Health Prognosis With Unlabeled and Low-Quality Data Exploitation

Accurate machinery health prognosis, also known as remaining useful life (RUL) prediction, is critical for preventing catastrophic accidents and implementing predictive maintenance strategies. This makes it a highly attractive research area. Many existing studies have been developed on unimodal data, yet such data can only provide a restricted perspective and incomplete health state monitoring. Some researchers seek to address this issue from a multimodal standpoint. While promising, these methods still have certain shortcomings: 1) the imbalance for unlabeled and low-quality data compared to well-labeled data is not considered, causing their potential underexploited; 2) information richness during fusion is insufficient, discarding many valuable original and subtle health state cues, and they fail to timely tackle unexpected online anomalies; and 3) correlations and complementary information across modalities are neglected. To address these challenges, a multimodal correlation-aware fusion framework is proposed for machinery health prognosis. The framework adopts a pretrain-finetune paradigm with two parts. The first part achieves effective exploitation of the unlabeled and low-quality multimodal data pieces. The second part, through degradation pattern recognition, enables the framework to bridge the gap between scarce multimodal labeled data and accurate RUL prediction. A real industrial multimodal dataset of milling cutters is applied to demonstrate the proposed framework. Results from a series of ablation experiments and comparisons with state-of-the-art prediction methods indicate the effectiveness of each key component within the framework and its overall superiority. The framework shows promise in adapting to more downstream industrial tasks, providing accurate and reliable insights from limited data resources.