基于随机非参数时间序列模型的统计导波SHM
Statistical guided-waves-based SHM via stochastic non-parametric time series models
摘要：
在过去的几十年中，基于导波的主动传感结构健康监测（SHM）中的损伤检测已经经历了多个发展时期。尽管如此，仍然存在着一些挑战，面临着目前的国家的最先进的方法，无论是在工业以及在研究和发展，包括低损伤灵敏度，缺乏鲁棒性的不确定性，需要用户定义的阈值，并在传感器网络的非均匀响应。在这项工作中，提出了一种新的统计框架的基础上使用超声导波主动传感SHM。该框架基于随机非参数时间序列模型及其相应的统计特性，以便通过使用适当的统计决策测试来提供健康的置信区间并实现准确和鲁棒的损伤检测。三种方法及相应的统计量（测试统计）以及决策制定方案，并通过使用三个不同复杂程度的试样进行实验评估：具有增长缺口的Al板，具有增加的重量以模拟局部损伤的碳纤维增强塑料（CFRP）板，以及用于开放导波项目的CFRP面板[1]，都装有压电传感器和一发一收结构。所提出的方法的性能进行比较，最先进的时域损伤指数（DI）。结果表明，所提出的方法的灵敏度和鲁棒性的增加，与传统的方法相比，具有更好的跟踪能力的损伤演化，即使是损伤非相交的致动器-传感器路径。总体而言，所提出的统计方法表现出更大的损伤灵敏度在不同的组件，增强了鲁棒性的不确定性，以及用户友好的应用程序。
英文摘要：
Damage detection in active-sensing, guided-waves-based Structural Health Monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exists a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a Carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damages, and the CFRP panel used in the Open Guided Waves project [1], all fitted with piezoelectric transducers and a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator-sensor paths. Overall, the proposed statistical methods exhibit greater damage sensitivity across different components, with enhanced robustness to uncertainty, as well as user-friendly application.
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