Baseline-free structural health monitoring in aeronautical structures under in-service conditions

This PhD research has developed innovative methodologies for Structural Health Monitoring (SHM), addressing key challenges in damage detection, and damage growth prediction under varying environmental and operational conditions. By integrating vibration- and guided-wave based methods and leveraging the multifunctionality of PZT transducers, the research enhances SHM systems' sensitivity to minor damage, independence from baseline data, and resilience to temperature variations. A hybrid SHM approach combining guided wave and vibration-based techniques is proposed to improve the performance of damage detection and localization, utilizing their complementary strengths for monitoring complex structures with greater accuracy and robustness. Building upon this foundation, a novel vibration-based SHM method is introduced, utilizing transmissibility functions (TFs) as temperature-insensitive features for damage detection. This approach represents a more advanced level of fusion by employing piezoelectric transducers (PZTs) for modal analysis, enabling reliable detection performance across diverse materials and environmental conditions. Furthermore, a comprehensive comparative analysis of linear and nonlinear guided wave methods, alongside vibration-based techniques, underscores their respective strengths and limitations in monitoring progressive damage. This analysis also provides critical validation for the proposed hybrid approach, demonstrating its effectiveness in addressing the challenges of damage detection under varying conditions. Finally, an innovative baseline-free framework is also presented for in-service crack detection and growth prediction. Using passive PZT transducers and advanced analytics, this approach enables early damage detection and precise crack quantification without baseline data, significantly enhancing in-service SHM applications. The contribution of this research to the advancement of SHM technologies by addressing key challenges about environmental variation, baseline dependency, and real-time monitoring.