基于非平稳时间序列模型的导波时变温度识别
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models
摘要：
现代民用、机械和航空结构正在向连续、在线和自动化维护模式过渡，以确保更高的安全性和可靠性。结构健康监测（SHM）领域在这方面发挥着关键作用，基于主动传感声超声导波的SHM技术由于其对结构中微小变化的潜在敏感性而显示出巨大的前景。然而，这些方法的鲁棒性和诊断能力在存在环境和操作可变性（例如温度变化）的情况下变得有限。为了克服这一困难，本文采用了一种新的随机时间序列框架来模拟温度变化下的导波传播。不同的随机时变时间序列模型，如递归最大似然时变自回归（RML-TAR）和递归最大似然时变自回归与外源激励（RML-TARX）模型被提出来建模和捕捉导波传播在变化的温度下的潜在动力学。身份查验程序的步骤和方面都有介绍和清楚的解释。然后，识别出的模型用于执行一步提前预测以及导波信号的“模拟”。为了从物理学角度获得洞察力，还建立了高保真有限元（FE）模型来模拟温度变化对导波传播的影响。最后，代理模型制定通过使用随机时变的RML-TARX模型，并在不同的温度下与有限元模型进行比较。
英文摘要：
Modern-day civil, mechanical, and aeronautical structures are transitioning towards a continuous, online, and automated maintenance paradigm in order to ensure increased safety and reliability. The field of structural health monitoring (SHM) is playing a key role in this respect and active sensing acousto-ultrasound guided-wave based SHM techniques have shown great promise due to their potential sensitivity to small changes in the structure. However, the methods' robustness and diagnosis capability become limited in the presence of environmental and operational variability such as changing temperature. In order to circumvent this difficulty, in this paper, a novel stochastic time series-based framework was adopted to model guided wave propagation under varying temperatures. Different stochastic time-varying time series models, such as Recursive Maximum Likelihood Time-varying Auto-Regressive (RML-TAR) and Recursive Maximum Likelihood Time-varying Auto-Regressive with Exogenous Excitation (RML-TARX) models are put forward to model and capture the underlying dynamics of guided wave propagation under varying temperatures. The steps and facets of the identification procedure are presented and clearly explained. Then the identified models are used to perform one-step-ahead prediction as well as "simulation" of the guided wave signals. In order to gain insight from a physics perspective, high-fidelity finite element (FE) models were also established to model the effect of temperature variation on guided wave propagation. Finally, surrogate models are formulated through the use of stochastic time-dependent RML-TARX models and compared with the FE models under varying temperatures.
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