Robust methods of inclusive outlier analysis for structural health monitoring
Robust methods of inclusive outlier analysis for structural health monitoring
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DOI：
10.1016/j.jsv.2014.05.012
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发表时间：
2014-09-29
影响因子：
4.7
通讯作者：
Worden, K.
中科院分区：
文献类型：
--
作者：
Dervilis, N.;Cross, E. J.;Worden, K.
这项工作的关键新元素是引入强大的多元统计方法到结构健康监测（SHM）领域通过使用最小协方差行列式估计（MCD）和最小体积封闭椭球（MVEE）。在本文中，强大的离群值统计进行了研究，主要集中在高层次的估计的“掩蔽效应”的包容性离群值，不仅确定存在或不存在的新奇的东西，这是根本的利益，但也检查正常的条件下设置的怀疑，它可能已经包括多个异常。通过在早期阶段识别和检测变异性，可以增加实现良好的概括和建立正确的正常状况分类器的前景。重要的是要强调，在实施算法时，受损和未受损状态数据之间没有先验划分，这比其他方法具有显着优势。总之，本文介绍了一种新的计划，SHM利用强大的多变量离群值统计，以调查，如果选择的功能是免费的多个离群值之前，这样的功能可以选择监督或无监督分析。(C)2014爱思唯尔有限公司版权所有。
The key novel element of this work is the introduction of robust multivariate statistical methods into the structural health monitoring (SHM) field through use of the minimum covariance determinant estimator (MCD) and the minimum volume enclosing ellipsoid (MVEE). In this paper, robust outlier statistics are investigated, focussed mainly on a high level estimation of the "masking effect" of inclusive outliers, not only for determining the presence or absence of novelty-something that is of fundamental interest but also to examine the normal condition set under the suspicion that it may already include multiple abnormalities. By identifying and detecting variability at an early stage, the prospects of achieving good generalisation and establishing a correct normal condition classifier may be increased. It is critical to highlight that there is no a priori division between the damaged and the undamaged condition data when the algorithms are implemented, offering a significant advantage over other methodologies. In summary, this paper introduces a new scheme for SHM by exploiting robust multivariate outlier statistics in order to investigate if the selected features are free from multiple outliers before such features can be selected for either supervised or unsupervised analysis. (C) 2014 Elsevier Ltd. All rights reserved.