用卷积网络仅凭响应数据就能判断系统模型类别，无需输入信息。
A convolutional neural network deep learning method for model class selection
- 仅用响应信号和类别标签训练一维卷积网络进行模型分类。
- 在微小阻尼或滞回变化下仍能准确识别线性与非线性系统的模型类。
- 适合结构健康监测，尤其适用于无法获取系统输入的场景。
本文提出一种新型深度卷积神经网络方法，仅通过单一自由度系统的响应信号及其类别信息，训练并验证一维卷积神经网络，实现对新信号模型类别的选择，无需系统输入信息或完整系统辨识。此外，研究还探讨了基于卡尔曼滤波的物理增强算法，利用加速度与位移的运动学约束融合响应信号。该方法在包含微小阻尼或滞回特性的线性与非线性动态系统上均表现出良好性能，并成功应用于三维建筑有限元模型，展现出在结构健康监测中的强大潜力。
原文摘要 · Abstract (English)
The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.
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