Wavelet-based outlier analysis for guided wave structural monitoring: Application to multi-wire strands
Wavelet-based outlier analysis for guided wave structural monitoring: Application to multi-wire strands
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DOI：
10.1016/j.jsv.2007.06.058
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发表时间：
2007-10-23
影响因子：
4.7
通讯作者：
Viola, Erasmo
中科院分区：
文献类型：
--
作者：
Rizzo, Piervincenzo;Sorrivi, Elisa;Viola, Erasmo
本文提出了一种基于离群值分析和小波变换的超声导波结构损伤检测方法。其基本思想是通过离散小波变换对超声信号进行降噪和压缩，利用相关小波系数构建一维或多维损伤指标。然后将损伤指数输入到离群值分析中，以检测代表结构缺陷的异常情况。通过从超声信号中提取基本信息，使损伤指标的维度保持在最小，以满足连续结构监测的需要。将一般框架应用于利用内置磁致伸缩超声转导装置检测七线股的缺口状缺陷。随机噪声以数字方式添加到原始超声波测量中，以创建基线（未损坏）条件和损坏条件的统计总体。此应用程序演示了与一维分析相比多维分析的有效性，同时将特征数量保持在4个以下。(C) 2007 Elsevier Ltd.版权所有。
\In this paper we describe a method based on outlier analysis and the wavelet transform for structural damage detection based on guided ultrasonic waves. The basic idea is to de-noise and compress the ultrasonic signals by the discrete wavelet transform and use the relevant wavelet coefficients to construct a unidimensional or multidimensional damage index. The damage index is then fed to an outlier analysis to detect anomalies that are representative of structural defects. By extracting the essential information from the ultrasonic signals, the dimension of the damage index is kept at a minimum, as desirable for continuous structural monitoring. The general framework is applied to the detection of notch-like defects in a seven-wire strand by using built-in magnetostrictive devices for ultrasound transduction. Random noise is digitally added to the raw ultrasonic measurements to create statistical populations of the baseline (undamaged) conditions and the damaged conditions. This application demonstrates the effectiveness of the multidimensional analysis compared to the unidimensional analysis, while keeping the number of features as low as four. (C) 2007 Elsevier Ltd. All rights reserved.