A comparative evaluation of two statistical analysis methods for damage detection using fibre optic sensor data

One of the commonly used optic sensing technologies is a point sensor with Fibre Bragg Grating (FBG), which is employed with an in‐house developed FBG interrogator. It is critical to couple such sensing capabilities with effective data analysis methods that can identify structural changes and detect possible damage. In this study, Robust Regression Analysis (RRA) and Cross Correlation Analysis (CCA) are employed to analyse strain data collected with FBG sensors that are installed on a 4‐span bridge type structure. In order to test the efficiency of these non‐parametric data analysis approaches, several tests are conducted with different damage scenarios in the laboratory environment. The efficiency of FBG sensors in conjunction with RRA and CCA algorithms for detection and localising damage are explored. Based on the findings, the RRA and CCA methods with FBGs can be expected to deliver promising results as to observing and detecting both local and global damage.