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首页> 外文期刊>Jordan Journal of Mechanical and Industrial Engineering >Damage Identification of Welded Structures Using Time Series Models and Exponentially Weighted Moving Average Control Charts
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Damage Identification of Welded Structures Using Time Series Models and Exponentially Weighted Moving Average Control Charts

机译：使用时间序列模型和指数加权移动平均控制图识别焊接结构的损伤

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The main aim of this paper is to demonstrate a new approach for the health monitoring of structures to identify the damage at earliest possible stage using the acceleration-time data obtained from the piezoelectric accelerometers. This paper presents a unique combination of time series models to extract the damage sensitive features and exponentially weighted moving average (EWMA) control charts to monitor the variations of the selected features. First, the damage sensitive features are extracted by fitting a time series prediction model called an auto-regressive (AR) model to the acceleration-time data obtained from the undamaged structure. Then the residual errors are calculated which quantify the difference between the actual acceleration-time data and the prediction from the AR model at each time interval is defined as the damage sensitive feature. The variation of these features is monitored using EWMA control charts. The applicability of the proposed damage identification approach is tested with the welded structure like cantilever plate. The damage is introduced to the test structure by cutting a slot in the weld using electrical discharge machining. Three damage levels are considered and named damage level zero, damage level one and damage level two. As the outliers are statistically significant in number and are increasing as the damage level increases, it is concluded from the EWMA control charts that this approach not only identifies the presence of damage but also sensitive to the severity of the damage.
机译：本文的主要目的是演示一种新的结构健康监测方法，以利用从压电加速度计获得的加速时间数据，尽早发现损坏。本文提出了时间序列模型的独特组合，以提取损伤敏感特征和指数加权移动平均（EWMA）控制图，以监视所选特征的变化。首先，通过将称为自回归（AR）模型的时间序列预测模型拟合到从未损坏结构获得的加速时间数据中，提取损伤敏感特征。然后，计算残留误差，该误差将实际加速时间数据与AR模型在每个时间间隔的预测之间的差异量化为损伤敏感特征。这些功能的变化使用EWMA控制图进行监控。所提出的损伤识别方法的适用性已通过焊接结构（如悬臂板）进行了测试。通过使用放电加工在焊缝中切出一个缝隙，将损坏引入测试结构。考虑了三个损坏级别，分别命名为零损坏级别，一损坏级别和二损坏级别。由于异常值在数量上具有统计意义，并且随着损坏程度的增加而增加，因此从EWMA控制图中可以得出结论，这种方法不仅可以识别损坏的存在，而且可以对损坏的严重程度敏感。

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