Outlier analysis of nonlinear solitary waves for health monitoring applications
Outlier analysis of nonlinear solitary waves for health monitoring applications
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用于健康监测应用的非线性孤立波异常值分析
DOI：
10.1177/1475921719876089
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Nasrollahi
中科院分区：
文献类型：
--
作者：
Bowen Zheng;P. Rizzo;A. Nasrollahi
基于高度非线性孤立波的产生和检测的结构健康监测/无损评估方法正在成为一种经济有效的技术，以监测或检查各种结构和材料。这些波具有传统超声波所没有的独特特性。离群值分析是一种统计工具，能够识别偏离基线数据的数据中的异常。虽然异常值分析已经得到了相当大的关注，缺陷检测使用模态数据，引导超声波，或其他无损方法，其应用程序的孤立波分析从未被探索。在本文中提出的研究中，使用离群值分析的不一致性检验和马氏平方距离进行了研究，以提高基于高度非线性孤立波的监测系统的损伤检测能力。进行了两个实验来证明该程序。在第一个实验中，一个厚钢板探测与孤立波换能器放置在板上方，和损伤进行了模拟的外物磁性附着在板的底部，在不同的距离换能器。在第二个实验中，将两块铝板放置在彼此之上，与顶板干燥接触，顶板受到局部的，大多数隐藏的缺陷。第一个实验中使用的传感器在第二个测试中被装入一个带轮子的小车中，以在离散位置扫描样品。对于这两个实验，从时间波形中提取一些特征，并将其馈送到将测试数据与一组基线数据进行比较的单变量和多变量分析中。结果表明，孤立点分析显著提高了孤立波损伤检测的能力。
The structural health monitoring/nondestructive evaluation method based on the generation and detection of highly nonlinear solitary waves is emerging as a cost-effective technique to monitor or inspect a variety of structures and materials. These waves possess unique characteristics not seen in conventional ultrasounds. Outlier analysis is a statistic tool able to identify anomalies in data that diverge from a set of baseline data. Although outlier analysis has received considerable attention for defect detection using modal data, guided ultrasonic waves, or other nondestructive approaches, its application for the analysis of solitary waves has never been explored. In the study presented in this article, the use of outlier analysis in terms of discordancy test and Mahalanobis squared distance was investigated to enhance the damage detection capability of a monitoring system based on highly nonlinear solitary waves. Two experiments were performed to demonstrate the procedure. In the first experiment, a thick steel plate was probed with a solitary wave transducer placed above the plate, and damage was simulated in terms of a foreign object magnetically attached to the bottom of the plate, at different distances from the transducer. In the second experiment, two aluminum plates were placed above each other in dry contact with the top plate subjected to localized, mostly hidden, defects. The transducer used in the first experiment was in this second test encased in a small cart with wheels to scan the sample at discrete positions. For both experiments, a few features were extracted from the time waveforms and fed to a univariate and a multivariate analysis that compared the testing data to a set of baseline data. The results show that the outlier analysis significantly improves the ability to detect damage using solitary waves.