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On-line updating Gaussian process measurement model for crack prognosis using the particle filter

机译：使用粒子过滤器在线更新高斯过程测量模型以预测裂纹

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摘要

The particle filter (PF) has shown great potential for on-line fatigue crack growth prognosis by combining crack measurements from structural health monitoring (SHM) techniques. In this method, a key problem is to construct the mapping between the feature extracted from SHM signals and the crack size. However, this mapping may be inaccurate since the data used for establishing the mapping is affected by uncertainties from sources like damage geometries, sensor placements, and boundary conditions. To deal with this problem, this paper proposes an on-line updating Gaussian process (GP) measurement model within the PF based crack prognosis framework. The GP measurement model outputs the mean and variance of the crack length corresponding to the feature of SHM signals, which are input into the PF for evaluating the posterior estimation of the crack length for crack prognosis. Then, this posterior estimation is sequentially appended to the GP dataset for updating the measurement model. Moreover, once crack inspection data is obtained, it is combined with existing SHM data for additional updating of the GP measurement model. Validations are performed on the fatigue test of attachment lug structures, in which the guided wave based SHM technique is applied for crack monitoring. As the cyclic load may cause intricate influences on the guided wave propagation, it is more difficult to quantify the crack length. The validation result shows that the on-line updating GP measurement model can effectively map the feature of SHM signals to the crack length, and result in accurate crack growth prognosis with the PF based method.
机译：通过结合结构健康监测（SHM）技术的裂纹测量结果，粒子过滤器（PF）在在线疲劳裂纹扩展预后方面显示出巨大潜力。在这种方法中，关键问题是构造从SHM信号提取的特征与裂纹尺寸之间的映射。但是，此映射可能不准确，因为用于建立映射的数据受到来自诸如损坏几何形状，传感器放置和边界条件等来源的不确定性的影响。为了解决这个问题，本文提出了一种基于PF裂纹预测框架的在线更新高斯过程（GP）测量模型。 GP测量模型输出对应于SHM信号特征的裂纹长度的均值和方差，将其输入到PF中以评估裂纹长​​度的后验估计以预测裂纹。然后，将该后验估计顺序附加到GP数据集，以更新测量模型。而且，一旦获得了裂纹检查数据，它将与现有的SHM数据结合起来，用于GP测量模型的其他更新。对附接凸耳结构的疲劳测试进行了验证，其中基于导波的SHM技术用于裂缝监测。由于周期性载荷可能会对导波传播产生复杂的影响，因此更加难以量化裂纹长度。验证结果表明，在线更新的GP测量模型可以有效地将SHM信号的特征映射到裂纹长度，并采用基于PF的方法可以准确预测裂纹扩展。

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