论文标题
通过非平稳时间序列模型在不同温度下对引导波传播的时变鉴定
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models
论文作者
论文摘要
现代民用，机械和航空结构正在向连续，在线和自动化的维护范式过渡，以确保安全性和可靠性提高。结构性健康监测（SHM）领域在这方面发挥了关键作用，并且基于基于指导波的SHM技术的主动传感声音启用了巨大的希望，因为它们对结构的小变化的潜在敏感性。但是，在存在环境和操作变异（例如温度变化）的情况下，方法的鲁棒性和诊断能力受到限制。为了规避这一难度，在本文中，采用了一种新型的基于随机时间序列的框架，以模拟不同温度下的引导波传播。不同的随机时间变化时间序列模型，例如递归的最大可能性时变时间变化自动回归（RML-TAR）和递归最大的最大似然时变时间，外源性激发（RML-TARX）模型被提出，以模拟和捕获下降的波动传播的底层动力学。介绍并清楚地说明了标识程序的步骤和方面。然后，已确定的模型用于执行指导波信号的一步预测以及“模拟”。为了从物理学的角度获得洞察力，还建立了高保真有限元（FE）模型，以模拟温度变化对指导波传播的影响。最后，通过使用随机时间依赖性RML-TARX模型来制定替代模型，并将其与不同温度下的Fe模型进行比较。
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.
