On the use of the inverse finite element method to enhance knowledge sharing in population-based structural health monitoring

Abstract

Efficient Structural Health Monitoring (SHM) is critical for ensuring safety and improving the operation and maintenance of aerospace structures. This study focusses on advanced shape-sensing methods, such as the inverse Finite Element Method (iFEM), which can estimate the complete displacement field of a structure based on a restricted number of strain measurements, fostering continuous and real-time monitoring. This approach additionally provides valuable insights into the dynamic behaviour of a structure by extracting its Frequency Response Functions (FRFs) and modal properties to perform vibration-based SHM. However, effectively extending SHM to a fleet or population of structures would require a significant amount of data for each one, which may be unavailable or incomplete. A population-based Structural Health Monitoring (PBSHM) strategy can solve data scarcity by sharing knowledge between similar structures via transfer-learning algorithms. In PBSHM, handling data from diverse sources is paramount for achieving accurate results. Therefore, this study integrates iFEM into the PBSHM framework, enhancing knowledge transfer by harmonising fibre-optic strain measurements to vibration-based features and providing reliable source data to inform diagnostics on similar structures. The proposed approach is validated on a population of laboratory-scale steel aircraft subjected to specific operating and damage conditions tested using three different sensor setups.