Noncontact Cross Domain Fault Diagnosis via Multisource 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 multimodal and heterogeneous industrial applications, research has increasingly focused on multisensor fusion and intelligent diagnostics. Although multisensor 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 feature fusion network 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 multimodal synergy, and revealing the complementary mechanism of multisource data. This study enhances prognostics and health management systems' engineering applicability and perception capabilities in multisensor industrial environments, providing reliable multimodal diagnostics to advance intelligent maintenance.