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首页> 外文期刊>Structural health monitoring >An adaptive guided wave-Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test
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An adaptive guided wave-Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test

机译：时变条件下用于损伤监测的自适应导波-高斯混合模型：在飞机全面疲劳试验中的验证

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摘要

Structural health monitoring technology has gradually developed from the research in laboratory to engineering validations and applications. However, the problem of reliable damage evaluation under time-varying conditions is a main obstacle for applying structural health monitoring to real aircraft structures. Among the existing structural health monitoring methods, the guided wave-based structural health monitoring method is popular but the time-varying problem needs to be addressed. Several methods have been proposed to deal with this problem but limitations remain. In this article, an adaptive guided wave-Gaussian mixture model-based damage monitoring method is proposed. It can be used online without any structural mechanical model and a priori knowledge of damage under time-varying conditions. With this method, a baseline guided wave-Gaussian mixture model is constructed first based on the guided wave features obtained under time-varying conditions when the structure is in healthy state. When a new guided wave feature is obtained during an online damage monitoring process, the guided wave-Gaussian mixture model is updated by an adaptive updating mechanism including dynamic learning and Gaussian components split-merge. The mixture probability structure of the guided wave-Gaussian mixture model and the number of Gaussian components can be optimized adaptively. Finally, a probability damage index is proposed to measure the degree of variation between the baseline guided wave-Gaussian mixture model and the online guided wave-Gaussian mixture model to reveal the damage-induced weak cumulative variation trend of the guided wave-Gaussian mixture model so as to increase the damage evaluation reliability. The method is validated in a full-scale aircraft fatigue test, and the results indicate that the reliable crack growth monitoring of the right landing gear spar and the left wing panel under the fatigue load condition is achieved.
机译：结构健康监测技术已从实验室研究逐渐发展为工程验证和应用。然而，在时变条件下进行可靠的损害评估的问题是将结构健康监测应用于实际飞机结构的主要障碍。在现有的结构健康监测方法中，基于导波的结构健康监测方法很流行，但时变问题需要解决。已经提出了几种方法来解决这个问题，但是仍然存在局限性。本文提出了一种基于自适应导波-高斯混合模型的损伤监测方法。可以在线使用它，而无需任何结构力学模型，也无需事先了解随时间变化的损坏情况。使用此方法，首先基于结构处于健康状态时在时变条件下获得的导波特征，构造基线导波-高斯混合模型。当在在线损伤监测过程中获得新的导波特征时，可通过自适应更新机制更新导波-高斯混合模型，其中包括动态学习和高斯分量拆分合并。导波-高斯混合模型的混合概率结构和高斯分量的数目可以自适应地优化。最后，提出了一种概率损伤指数来衡量基线导波-高斯混合模型与在线导波-高斯混合模型之间的变化程度，以揭示损伤引起的导波-高斯混合模型的弱累积变化趋势。从而提高损伤评估的可靠性。该方法在飞机全面疲劳试验中得到验证，结果表明，在疲劳载荷条件下，对右起落架翼梁和左翼板的裂纹扩展进行了可靠的监测。

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