通过广义总体形式模拟基于振动的PBSHM的可变性
Modelling variability in vibration-based PBSHM via a generalised population form
摘要：
结构健康监测（SHM）在过去的三十年里一直是一个活跃的研究领域，并在此期间积累了一些关键的进展，可以在文献中看到。然而，由于损伤状态数据的缺乏、操作和环境波动、可重复性问题以及边界条件的变化，SHM仍然面临挑战。这些问题表现为捕获的功能不一致，可能对实际实现产生巨大影响，但更重要的是，对技术的推广产生巨大影响。人口为基础的SHM已被设计为解决这些问题的建模和传输缺失的信息，使用从类似结构的群体收集的数据。在这项工作中，振动数据收集从四个健康的，名义上相同的，全尺寸复合材料直升机叶片。制造差异（例如，几何形状和/或材料特性的微小差异），在叶片之间呈现为它们的结构动力学的可变性，这对于基于来自振动数据的机器学习的SHM来说可能是非常有问题的。这项工作的目的是解决这种变异性，通过定义一个通用模型的叶片的频率响应函数，称为形式，使用高斯过程的混合物。
英文摘要：
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, vibration data were collected from four healthy, nominally-identical, full-scale composite helicopter blades. Manufacturing differences (e.g., slight differences in geometry and/or material properties), among the blades presented as variability in their structural dynamics, which can be very problematic for SHM based on machine learning from vibration data. This work aims to address this variability by defining a general model for the frequency response functions of the blades, called a form, using mixtures of Gaussian processes.
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