Abstract This study explored the sensor clustering-based damage detection beyond the free-vibration limitation to allow for the direct utilisation of time-series for damage identification under ambient vibration. In the proposed method, a dense sensor network is clustered and each sensor cluster is represented by nonlinear autoregressive with exogenous inputs (NARX) model, which is developed in a black-box manner via an artificial neural network. Damage detection is achieved through a new damage sensitive feature which is formulated from the NARX neural network prediction error. The efficiency of the proposed methodology is assessed firstly using test data of an 8-DOF system and later by conducting an experimental study on scaled steel arch laboratory models subjected to various damage cases. The obtained results reveal that the proposed method can satisfactorily detect, localise, and estimate damage severity in the test structure.

中文翻译：

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基于传感器聚类的环境振动下结构损伤识别方法

摘要 本研究探索了超越自由振动限制的基于传感器聚类的损伤检测，以允许直接利用时间序列进行环境振动下的损伤识别。在所提出的方法中，一个密集的传感器网络被聚类，每个传感器集群由非线性自回归（NARX）模型表示，该模型是通过人工神经网络以黑盒方式开发的。损伤检测是通过一种新的损伤敏感特征实现的，该特征是根据 NARX 神经网络预测误差制定的。首先使用 8 自由度系统的测试数据评估所提出方法的效率，然后通过对受到各种损坏情况的缩放钢拱实验室模型进行实验研究来评估。