Investigating the Effects of Ambient Temperature on Feature Consistency in Vibration-Based SHM

Abstract

Structural health monitoring (SHM) systems have been implemented across multiple engineering applications, and SHM remains an active area of research addressing the improved safety, reliability, and management of these structures. Several challenges, however, have limited the practical implementation and generalisation of SHM technologies, such as operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These inconsistencies can be problematic for SHM based on machine learning, as healthy states may be incorrectly flagged as damaged, or damaged states may be misclassified as normal variations. Likewise, manufacturing differences can result in variation among similar structures. Accounting for these variations is especially important for a population-based approach to SHM (PBSHM), which seeks to transfer valuable information, including normal operating conditions and damage states, across similar structures. This work aims to quantify this variability, and evaluate the applicability of SHM when these deviations occur. In this paper, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of full-scale, composite glider wings. Tests were performed at multiple ambient temperatures, and with real and simulated damage conditions. The frequency response functions of the wings are examined to identify changes in natural frequency.