Using the inverse finite‐element method to harmonise classical modal analysis with fibre‐optic strain data for robust population‐based structural health monitoring

Abstract

AbstractVibration‐based approaches to structural health monitoring (SHM) gained increasing significance for assessing the behaviour of existing structures because of their non‐intrusive nature and high sensitivity to damage. However, data availability often limits the application of SHM approaches. The population‐based structural health monitoring (PBSHM) theory addresses this challenge, enhancing diagnostic inferences by sharing knowledge across a population of similar structures. In real‐life scenarios, sharing data from distinct structures requires dealing with results obtained with different experimental setups, multiple sensors, input choices and acquisition systems. Therefore, it is crucial to harmonise various features to achieve accurate and reliable results. The present study presents the results of a classic experimental modal analysis (EMA) using scanning laser Doppler vibrometer (SLDV) measurements and a strain‐based EMA conducted using high‐definition distributed fibre‐optic strain sensors. The experimental case study of a laboratory‐scale steel aircraft subjected to specific operating and damage conditions is introduced, allowing for a comprehensive discussion of the features extracted from the two EMA techniques, which can also be generalised to structures within different domains. This research highlights the advantages and limitations of fibre‐optic‐based EMA compared to classic methods, as fibre‐optic strain sensors offer a cost‐effective alternative to accelerometers or SLDV for dynamic testing. Furthermore, the feasibility of employing the inverse finite‐element method (iFEM) in the dynamic domain is investigated. This method can estimate the whole displacement field of a structure from a limited number of strain values, thus harmonising strain measurements with the SLDV measurements. By analysing the features extracted from different EMA techniques within the PBSHM framework, this study contributes to advancing the understanding and application of the PBSHM approach in diverse experimental scenarios, laying the foundation for further investigation of features and adequate methods for sharing damage‐state knowledge across a population of structures.