An adaptive learning damage estimation method for structural health monitoring
An adaptive learning damage estimation method for structural health monitoring
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DOI：
10.1177/1045389x14522531
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发表时间：
2015-01-01
影响因子：
2.7
通讯作者：
Chattopadhyay, Aditi
中科院分区：
文献类型：
--
作者：
Chakraborty, Debejyo;Kovvali, Narayan;Chattopadhyay, Aditi
结构健康监测是许多民用基础设施和航空航天应用中感兴趣的重要问题。在过去的几十年里，许多技术已经被研究，以解决在结构部件的损伤检测，估计和分类。然而，在开发真实世界的损伤识别系统中，一个关键的挑战是由于不断变化的环境和操作条件而引起的可变性。基于静态建模框架的传统统计方法在动态和快速变化的环境中可能被证明是不够的，特别是当没有足够的数据时。本文提出了一种新的自适应学习结构损伤估计方法，该方法允许随机模型随时变条件不断变化。自适应学习框架基于Dirichlet过程（DP）混合模型的使用，该模型提供了自动调整数据结构的能力。具体地，从周期性收集的结构数据（测量的传感器信号）中提取时频特征，所述结构数据是对材料的超声激励的响应。然后使用DP混合物模型对这些进行建模，该模型允许不断增长的（可能是无限的）混合物组分或潜在簇的数量。结合物理损伤增长模型的输入，自适应识别的集群中使用的状态空间设置，以有效地估计在不同的外部条件下的结构内的损伤状态。此外，数据选择方法的实施，使明智的选择信息测量最大的性能。所提出的算法的实用性证明了应用程序进行变幅循环载荷的铝紧凑拉伸试样的疲劳损伤的估计。
Structural health monitoring is an important problem of interest in many civil infrastructure and aerospace applications. In the last few decades, many techniques have been investigated to address the detection, estimation, and classification of damage in structural components. One of the key challenges in the development of real-world damage identification systems, however, is variability due to changing environmental and operational conditions. Conventional statistical methods based on static modeling frameworks can prove to be inadequate in a dynamic and fast changing environment, especially when a sufficient amount of data is not available. In this paper, a novel adaptive learning structural damage estimation method is proposed in which the stochastic models are allowed to perpetually change with the time-varying conditions. The adaptive learning framework is based on the use of Dirichlet process (DP) mixture models, which provide the capability of automatically adjusting to structure within the data. Specifically, time-frequency features are extracted from periodically collected structural data (measured sensor signals), that are responses to ultrasonic excitation of the material. These are then modeled using a DP mixture model that allows for a growing, possibly infinite, number of mixture components or latent clusters. Combined with input from physically based damage growth models, the adaptively identified clusters are used in a state-space setting to effectively estimate damage states within the structure under varying external conditions. Additionally, a data selection methodology is implemented to enable judicious selection of informative measurements for maximum performance. The utility of the proposed algorithm is demonstrated by application to the estimation of fatigue-induced damage in an aluminum compact tension sample subjected to variable-amplitude cyclic loading.