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Structural damage diagnosis under varying environmental conditions—part II: local PCA for non-linear cases

机译：在变化的环境条件下的结构损坏诊断—第二部分：用于非线性情况的局部PCA

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摘要

It is well known that changes in vibration features of structures due to damage may be masked by the effects of environmental variations. This influence has to be eliminated in the structural health-monitoring process, especially when a long-term in situ monitoring is expected. In the companion paper a linear method based on principal component analysis (PCA) has been proposed and has shown encouraging results for linear or even weakly non-linear cases. The present paper concerns a further extension of the proposed method to handle non-linear cases, which may be encountered in some complex structures. The method involves a two-step procedure, namely a clustering of the data space into several regions and then the application of PCA in each local region. The application of local PCA allows performing a piecewise linearisation of the non-linear problem. A close look at the choice of the distortion function used in data clustering leads to two new clustering strategies. Whereas the first strategy is specifically suitable for the application treated in this paper, the second one is more general. The local PCA-based damage detection method is applied for the structural health monitoring of a real bridge using vibration data measured in situ over a one-year period.
机译：众所周知，由于损坏而引起的结构振动特性的变化可能被环境变化的影响所掩盖。在结构健康监测过程中，必须消除这种影响，尤其是在需要长期就地监测的情况下。在随附的论文中，提出了一种基于主成分分析（PCA）的线性方法，该方法对于线性甚至弱非线性情况都显示出令人鼓舞的结果。本文涉及所提出方法的进一步扩展，以处理非线性情况，这在某些复杂结构中可能会遇到。该方法涉及两步过程，即将数据空间聚集到几个区域中，然后在每个局部区域中应用PCA。局部PCA的应用允许对非线性问题进行分段线性化。仔细研究数据聚类中使用的失真函数的选择会导致两种新的聚类策略。尽管第一种策略特别适合本文处理的应用程序，但是第二种策略更通用。基于本地PCA的损伤检测方法被用于使用一年中就地测量的振动数据对真实桥梁进行结构健康监测。

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