Adaptive Multi-View Hypergraph Learning for Cross-Condition Bearing Fault Diagnosis

Reliable bearing fault diagnosis across diverse operating conditions remains a fundamental challenge in intelligent maintenance. Traditional data-driven models often struggle to generalize due to the limited ability to represent complex and heterogeneous feature relationships. To address this issue, this paper presents an Adaptive Multi-view Hypergraph Learning (AMH) framework for cross-condition bearing fault diagnosis. The proposed approach first constructs multiple feature views from time-domain, frequency-domain, and time–frequency representations to capture complementary diagnostic information. Within each view, an adaptive hyperedge generation strategy is introduced to dynamically model high-order correlations by jointly considering feature similarity and operating condition relevance. The resulting hypergraph embeddings are then integrated through an attention-based fusion module that adaptively emphasizes the most informative views for fault classification. Extensive experiments on the Case Western Reserve University and Ottawa bearing datasets demonstrate that AMH consistently outperforms conventional graph-based and deep learning baselines in terms of classification precision, recall, and F1-score under cross-condition settings. The ablation studies further confirm the importance of adaptive hyperedge construction and attention-guided multi-view fusion in improving robustness and generalization. These results highlight the strong potential of the proposed framework for practical intelligent fault diagnosis in complex industrial environments.