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首页> 外文期刊>International Journal of Sustainable Materials and Structural Systems >Robust methods for outlier detection and regression for SHM applications
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Robust methods for outlier detection and regression for SHM applications

机译：用于SHM应用程序的异常值检测和回归的鲁棒方法

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摘要

In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the 'masking effect' of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors.
机译：在本文中，针对基于数据的结构健康监测（SHM）方法，提出了可靠的统计方法。最初的讨论重点是从高层次上消除包容性异常值的“掩盖效应”。当采用无监督学习形式的新颖性检测作为损害诊断的手段时，通常会出现多个异常值。然后必须非常小心地处理结构的运行或环境条件中的良性变化，因为它们有可能导致误报。结果表明，稳健回归领域的最新发展可以提供一种探索和可视化SHM数据的手段，作为探索异常值不同特征并消除良性变化影响的工具。该论文无论如何都不是一项调查。它是作者近期工作的概述和总结。

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