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An adaptive PCA‐based method for more reliable ultrasonic guided waves SHM: Data‐driven modeling and experimental validation in high attenuating medium
Structural Control and Health Monitoring ( IF 5.4 ) Pub Date : 2020-10-11 , DOI: 10.1002/stc.2634
Mahjoub El Mountassir 1, 2 , Slah Yaacoubi 1 , Gilles Mourot 2 , Didier Maquin 2
Affiliation  

This paper proposes a monitoring method for defect detection and localization in a structure operating under environmental and operational conditions (EOCs) variation. This method is based on a model obtained using PCA of the healthy state data. To account for variation in EOCs, more particularly temperature change, the model is updated using a moving window over the collected signals. Defect detection was performed by calculating the squared prediction error between the current measured signal and its estimation predicted by the model. Once a defect is detected, its localization is performed by applying the PCA‐based model on a sliding window over the signal. The test and validation of the proposed method were achieved on two databases collected from two pipeline segments where each one has high attenuating composite reparation. The first database was built under small temperature variation, whereas the second one was under a relatively high temperature variation. To simulate a minor corrosion, a relatively small defect was created by removing material inside each pipeline segment. To mimic its time evolution in real world, this corrosion‐like defect was grown in different steps. The data were collected using ultrasonic guided waves (UGW) technique. Despite the high attenuation caused by the composite reparation, the specific placement of the defect, and its small size, it was successfully detected and localized. The proposed method for defect detection is not limited to UGW and could be applied to any active SHM technique that provides time signals.

中文翻译:

基于PCA的自适应方法可实现更可靠的超声波导波SHM:高衰减介质中的数据驱动建模和实验验证

本文提出了一种监测方法,用于在环境和操作条件(EOCs)变化下操作的结构中的缺陷检测和定位。该方法基于使用健康状态数据的PCA获得的模型。为了说明EOC的变化,尤其是温度变化,使用在收集到的信号上移动的窗口来更新模型。通过计算当前测量信号与模型预测的估计值之间的平方预测误差来执行缺陷检测。一旦检测到缺陷,就可以通过在信号上的滑动窗口上应用基于PCA的模型来进行缺陷定位。在从两个管道段收集的两个数据库中实现了该方法的测试和验证,其中每个管道具有高衰减复合修复。第一个数据库是在较小的温度变化下建立的,而第二个数据库是在较高的温度变化下建立的。为了模拟轻微腐蚀,通过去除每个管道段内的材料来创建相对较小的缺陷。为了模拟其在现实世界中的时间演变,这种腐蚀状缺陷是在不同的步骤中生长的。使用超声导波(UGW)技术收集数据。尽管由复合材料修复引起的高衰减,缺陷的特定位置以及尺寸很小,但仍可以成功地检测和定位缺陷。所提出的用于缺陷检测的方法不限于UGW,并且可以应用于提供时间信号的任何有源SHM技术。为了模拟轻微腐蚀,通过去除每个管道段内的材料来创建相对较小的缺陷。为了模拟其在现实世界中的时间演变,这种腐蚀状缺陷是在不同的步骤中生长的。使用超声导波(UGW)技术收集数据。尽管由复合材料修复引起的高衰减,缺陷的特定位置以及尺寸很小,但仍可以成功地检测和定位缺陷。所提出的用于缺陷检测的方法不限于UGW,并且可以应用于提供时间信号的任何有源SHM技术。为了模拟轻微腐蚀,通过去除每个管道段内的材料来创建相对较小的缺陷。为了模拟其在现实世界中的时间演变,这种腐蚀状缺陷是在不同的步骤中生长的。使用超声导波(UGW)技术收集数据。尽管由复合材料修复引起的高衰减,缺陷的特定位置以及尺寸很小,但仍可以成功地检测和定位缺陷。所提出的用于缺陷检测的方法不限于UGW,并且可以应用于提供时间信号的任何有源SHM技术。使用超声导波(UGW)技术收集数据。尽管由复合材料修复引起的高衰减,缺陷的特定位置以及尺寸很小,但仍可以成功地检测和定位缺陷。所提出的用于缺陷检测的方法不限于UGW,并且可以应用于提供时间信号的任何有源SHM技术。使用超声导波(UGW)技术收集数据。尽管由复合材料修复引起的高衰减,缺陷的特定位置以及尺寸很小,但仍可以成功地检测和定位缺陷。所提出的用于缺陷检测的方法不限于UGW,并且可以应用于提供时间信号的任何有源SHM技术。
更新日期:2020-12-20
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