Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of four healthy, full-scale composite helicopter blades. During the tests, variability was introduced by adjusting boundary conditions between each testing repetition. It is well known that changes of boundary conditions, even from careful repositioning of the structure, can alter selected feature’s properties, changing dynamic responses from normal condition and thus raising false alarms which degrade the effectiveness of SHM. In addition, nominally-identical structures may have slight differences in geometry and/or material properties. These variations can present as changes in the dynamic characteristics of the structure, which can be very problematic for SHM based on machine learning. This paper demonstrates the applicability of SHM when such deviations occur. In this work, a normal condition for the set of helicopter blades is established and tested via a point-wise outlier analysis approach and by defining a general model for the blades, called a population form, using Gaussian process regression.
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.
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