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首页> 外文会议>International Conference on Uncertainty in Structural Dynamics;International Conference on Noise and Vibration Engineering >Machine learning and sensor swarm for structural health monitoring of a bridge
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Machine learning and sensor swarm for structural health monitoring of a bridge

机译：机器学习与传感器群，用于桥梁的结构健康监测

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The present work proposes an unsupervised early-stage damage detection method, which relies on the combined application of the Principal Component Analysis, for features extraction and dimensionality reduction, and Symbolic Data Analysis, for automatically cluster different patterns. The structure considered is a Warren truss bridge, which is numerically simulated by a Finite Element Model. It is excited by a thermal cycle and a static load; the damage is modelled as a sudden reduction of the area of the section. The validity of the proposed algorithm is numerically tested over one month of vibration data. The damage is properly identified by some PCAs; furthermore, Symbolic Data Analysis allows an effective clustering of damaged and undamaged PCA samples. Robustness of the algorithm is tested at different noise level, timing of damage, damage position and depth, the influence of the sensors' number is also tested.
机译：本工作提出了一种无调节的早期损伤检测方法，它依赖于主成分分析的组合应用，适用于特征提取和减少，符号数据分析，用于自动集群不同的模式。 所考虑的结构是沃伦桁架桥，其通过有限元模型进行数值模拟。 它是热循环和静电载荷的兴奋; 损坏被建模为部分区域的突然减小。 所提出的算法的有效性在数月的振动数据上进行了数值测试。 一些PCAS识别损害; 此外，符号数据分析允许有效聚类的损坏和未损坏的PCA样本。 算法的鲁棒性在不同的噪声水平上测试，损坏的定时，损坏位置和深度，传感器数量的影响也在测试。

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