一种结合长短期记忆网络与扩展卡尔曼滤波的线性时变系统快速自然频率和阻尼比识别方法
Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
这是篇挺有意思的论文，用机器学习结合物理模型来识别时变系统的频率和阻尼，对风力涡轮机这类设备可能有用。
这项工作提出了一种物理增强的机器学习方法，用于在时变工作条件下对线性时变（LTV）系统的系统识别，特别是针对快速变化的自然频率和阻尼比。该方法通过结合长短期记忆网络与扩展卡尔曼滤波器（EKF）来实现。研究使用振动数据（位移和速度测量值）、模态阻尼比的知识以及基于物理的模型来生成近似自然频率的时间依赖性模型。该方法通过使用来自2叶片海上风力涡轮机有限元模型的合成数据进行了验证，该系统在真实环境和操作条件下显示出由于操作条件而快速变化的频率，其识别由于风浪载荷而特别具有挑战性。该方法的鲁棒性在假设系统信息不正确（例如阻尼比）的情况下进行了评估。该方法在不同环境和操作条件下进行了评估，以展示其在不同运行模式下的适用性。结果表明，该方法可以以最大均方根误差0.0012 Hz准确识别所选的快速变化自然频率，即1阶前-后（FA-1）模态。结果表明，基于EKF估计训练的模型依赖于准确的阻尼值，而基于物理数据的模型对不正确的阻尼假设具有鲁棒性。该方法被扩展到所选模式的阻尼比识别，通过估计基于EKF估计和基于物理数据的模型之间的均方根误差来实现。结果表明，该方法可以使用网格搜索对FA-1模态的阻尼比提供良好的近似，优于基于协方差驱动随机子空间识别的方法。
Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Kalman Filter (EKF). The proposed approach uses vibration data (displacement and velocity measurements), domain knowledge of modal damping ratios, and a physics-based model that can yield an approximate natural frequencies time-dependency model. The approach is validated using synthetic data generated from a finite element model of a 2-blade offshore wind turbine under realistic environmental and operating conditions. This system displays fast time-varying frequencies due to operating conditions, whose identification is particularly challenging because of the wind and wave loading. The robustness of the proposed approach is assessed under assumed incorrect system information (e.g. damping ratio). The proposed approach is evaluated across different environmental and operating conditions to show its applicability to different operating regimes. The results show the approach can accurately identify the selected fast-varying natural frequency, 1st Fore-Aft (FA-1) mode, with a maximum root mean square error of 0.0012 Hz. The results demonstrate that the model trained on EKF estimates depends on accurate damping values, whereas the model trained on physics-based data exhibits robustness to incorrect damping assumptions. The approach is extended to damping ratio identification for the selected mode by estimating the root mean square error between models trained on EKF estimates and physics-based data. The results show that the approach can yield a good approximation of the FA-1 mode damping ratio using grid search, offering an improvement over covariance-driven stochastic subspace identification.
