A machine‐learning approach to load‐monitoring based on guided waves

Abstract

The research field of structural health monitoring (SHM) describes the way to assess the structural integrity. The research objectives are broadly diversified, which are all addressing the improvement of SHM technologies. It is important to optimise the capability of sensor networks by applying intelligent signal processing models. These intelligent systems predict the health state based on acquired data in a real-time environment. Piezoelectric sensor networks and guided-wave based analyses are combined with machine learning to create an efficient load-monitoring routine. This load-monitoring approach is chosen to track any changes in the environmental conditions, which causes most of the problems in reference signal based damage detection. Therefore, the proposed load-monitoring method can be used to compensate the influence of external changes on the acquired signals and improve the performance and robustness of future SHM systems.

Guided wave‐based damage detection in solids using computational intelligence

“…In this context, a guided wave-based approach is presented to monitor the global health state of an aluminium plate [2]. The plate is equipped with piezoelectric elements to generate waves and to measure the propagated waves [3]. The elastic wave will propagate through the structure and any kind of damage will cause a change in the wave pattern, which can be classified by the help of a support vector machine (SVM) [4].…”