Non-contact Cross Domain Fault Diagnosis via Multi-Source Heterogeneous Data Fusion and Global Imbalance Awareness

Gas storage facilities have long faced bottlenecks in the core control systems of high-power compressors, where operational safety, efficiency, and automation under complex conditions remain key challenges. Traditional single-sensor monitoring offers limited perception and low data utilization, falling short of ensuring reliable operation and intelligent decision-making. With AI engineering advancing toward multi modal and heterogeneous industrial applications, research has increasingly focused on multi-sensor fusion and intelligent diagnostics. Although multi-sensor networks provide complementary information and enhanced perception, issues like data heterogeneity, class imbalance, and cross-domain distribution differences continue to constrain diagnostic performance. To address these issues, a Dual-branch Heterogeneous Synergistic Network (DHSNet) is proposed to achieve cross-modal fusion of vibration signals and infrared thermal imaging signals. The framework incorporates a cross domain adaptation strategy to enhance domain-invariant feature learning and a dynamic focal loss function to adaptively adjust class weights based on real-time output indicators, mitigating the impact of sample imbalance. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 93.27% in cross-load transfer tasks, outperforming other methods used in imbalanced scenarios by over 4%. Besides, the proposed method maintains robust diagnostic performance exceeding 90% accuracy across most load transfer scenarios, even under moderately imbalanced data conditions. Feature contribution analysis further validates the effectiveness of multi modal synergy, and revealing the complementary mechanism of multi-source data. This study enhances Prognostics and Health Management (PHM) systems' engineering applicability and perception capabilities in multi-sensor industrial environments, providing reliable multi-modal diagnostics to advance intelligent maintenance.