Optimizing Sensor Paths for Enhanced Damage Detection in Large Composite Stiffened Panels - A Multi-Objective Approach

Abstract

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in Structural Health Monitoring (SHM) sensor networks using Archived Multi-Objective Simulated Annealing (AMOSA). Using all of the sensor paths within a sensor network may not always be beneficial and could impair damage detection accuracy. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available, and may not result in optimal path selection. Therefore, this work proposes a novel automatic procedure for optimising sensor paths to maximise coverage level and damage detection accuracy, and minimise overall signal noise. This procedure was tested on a real-world large composite stiffened panel with many frames and stiffeners. Compared to using all of the available sensor paths, the optimized network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide 35% higher damage detection accuracy compared to a network designed based on prior expert knowledge. As a result, this novel procedure has the capability to design high-performing SHM sensor path networks for structures with complex geometries, but without the need for prior expert knowledge, making SHM more accessible to the engineering community.