Discerning the spurious modes for time-invariant or extremely slow time-varying structures with time-consuming tools (stabilization diagram and clustering), which is the main task of the automated modal identification, has been developed in last years. However, there is still a challenge the recursive identification of the linear time-varying structures. This study presents a single-mode recursive validation method for the recursive identification of the linear time-varying structures. Since one major issue is that the properties of the physical modes are time-varying, the proposed method extracts the information about the current state of the time-varying structures from the nonstationary vibration responses via deep learning method for the separation between physical and spurious modes. For the sake of effective elimination to spurious modes and control of computation efforts, the proposed method utilizes the application-dependent prior knowledge rather than iterations or high-dimensional optimizations, with robustness to hyperparameters. It can be combined with any parametric system identification method. A time-varying stiffness numerical example and a time-varying mass distribution experimental example illustrate the performance of the proposed modal validation method under various time-varying processes.

中文翻译：

---

一种基于先验知识的线性时变结构模态识别的单模递归验证方法

使用耗时的工具（稳定图和聚类）识别时不变或极慢时变结构的虚假模式，这是自动模态识别的主要任务，已在去年开发。然而，线性时变结构的递归识别仍然存在挑战。本研究提出了一种用于递归识别线性时变结构的单模递归验证方法。由于一个主要问题是物理模式的特性是时变的，所提出的方法通过深度学习方法从非平稳振动响应中提取有关时变结构当前状态的信息，以区分物理模式和杂散模式. 为了有效消除虚假模式和控制计算工作，所提出的方法利用依赖于应用程序的先验知识而不是迭代或高维优化，对超参数具有鲁棒性。它可以与任何参数系统识别方法结合使用。时变刚度数值示例和时变质量分布实验示例说明了所提出的模态验证方法在各种时变过程下的性能。