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Structural Health Monitoring under Nonlinear Environmental or Operational Influences

机译：非线性环境或运行影响下的结构健康监测

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Vibration-based structural health monitoring is based on detecting changes in the dynamic characteristics of the structure. It is well known that environmental or operational variations can also have an influence on the vibration properties. If these effects are not taken into account, they can result in false indications of damage. If the environmental or operational variations cause nonlinear effects, they can be compensated using a Gaussian mixture model (GMM) without the measurement of the underlying variables. The number of Gaussian components can also be estimated. For the local linear components, minimum mean square error (MMSE) estimation is applied to eliminate the environmental or operational influences. Damage is detected from the residuals after applying principal component analysis (PCA). Control charts are used for novelty detection. The proposed approach is validated using simulated data and the identified lowest natural frequencies of the Z24 Bridge under temperature variation. Nonlinear models are most effective if the data dimensionality is low. On the other hand, linear models often outperform nonlinear models for high-dimensional data.
机译：基于振动的结构健康监测基于检测结构动态特性的变化。众所周知，环境或操作上的变化也会对振动特性产生影响。如果不考虑这些影响，则可能导致错误的损坏迹象。如果环境或操作变化引起非线性影响，则可以使用高斯混合模型（GMM）进行补偿，而无需测量基础变量。高斯分量的数量也可以估算。对于局部线性分量，应用最小均方误差（MMSE）估计来消除环境或操作影响。应用主成分分析（PCA）后，会从残渣中检测出损坏。控制图用于新颖性检测。使用模拟数据和在温度变化下识别出的Z24桥的最低固有频率对所提出的方法进行了验证。如果数据维数较低，则非线性模型最有效。另一方面，对于高维数据，线性模型通常优于非线性模型。

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