线性时变系统快速变化固有频率与阻尼比辨识
Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
- University of Cambridge（剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种结合LSTM与扩展卡尔曼滤波的物理增强机器学习方法，用于辨识线性时变系统的快速变化固有频率和阻尼比，在海上风机合成数据上验证，FA-1模态频率辨识最大RMSE为0.0012 Hz，且对错误阻尼假设具有鲁棒性。
AI中文摘要：
本文提出了一种物理增强的机器学习方法，用于在时变运行条件下对线性时变（LTV）系统的快速变化固有频率和阻尼比进行系统辨识，该方法将长短期记忆网络与扩展卡尔曼滤波器（EKF）相结合。所提方法利用振动数据（位移和速度测量）、模态阻尼比的领域知识以及能够提供近似固有频率时变模型的基于物理的模型。该方法使用由2叶片海上风力发电机在真实环境和运行条件下的有限元模型生成的合成数据进行验证。该系统由于运行条件而表现出快速时变的频率，其辨识因风浪载荷而特别具有挑战性。在假设系统信息不正确（例如阻尼比）的情况下评估了所提方法的鲁棒性。所提方法在不同环境和运行条件下进行评估，以展示其在不同运行工况下的适用性。结果表明，该方法能够准确辨识选定的快速变化固有频率，即1阶前后（FA-1）模态，最大均方根误差为0.0012 Hz。结果证明，基于EKF估计训练的模型依赖于准确的阻尼值，而基于物理数据训练的模型对不正确的阻尼假设具有鲁棒性。该方法扩展到所选模态的阻尼比辨识，通过估计基于EKF估计和基于物理数据训练的模型之间的均方根误差。结果表明，该方法通过网格搜索能够对FA-1模态阻尼比提供良好的近似，优于协方差驱动的随机子空间辨识。
英文摘要：
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.
