Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.

中文翻译：

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使用模态分析的基于机器学习的新颖性检测

在许多结构评估方法中，模态特征的变化被认为是一种公认​​的损伤检测方法。但是，环境或运营变化的存在可能会污染基线，并妨碍对变化进行可靠的评估。近年来，机器学习算法的使用在结构健康界引起了人们的兴趣，特别是由于它们在消除环境不确定性方面的能力和成功。本文提出了一种端到端架构，可以通过使用机器学习算法来可靠地检测损坏。所提出的方法简化了（a）结构响应数据的收集，（b）使用系统识别的模态分析，（c）学习模型和（d）新奇检测。拟议的系统旨在提取可访问的模态参数的潜在特征，例如在温度不确定性下在未损坏的目标结构上测得的固有频率和模态形状，并使用成熟的机器学习方法重建与原始特征相似的新表示形式。损坏检测。测量参数和重构参数之间的偏差（也称为新奇指数）是检测系统中关键变化的基本信息。通过分析从有限元模型和实验结构获得的结构响应数据来评估该方法。对于该方法的机器学习组件，将同时检查主成分分析（PCA）和自动编码器（AE）。尽管在文献中众所周知，模态形状是一个经过充分研究的损伤指标，但据我们所知，这项研究是首次使用PCA和AE应用无监督机器学习来利用模态形状以及自然频率来进行有效损伤检测。将该管道的检测性能与类似的方法进行了比较，在该方法中，其学习模型未利用模式形状。结果表明，当在学习算法的训练中使用模式形状时，温度变化下的损伤检测的有效性显着提高。特别是对于较小的损害，所提出的算法在区分系统变化方面表现更好。这项研究是首次使用PCA和AE进行无监督机器学习，以利用自然频率以外的模式形状进行有效的损伤检测。将该管道的检测性能与类似的方法进行了比较，在该方法中，其学习模型未利用模式形状。结果表明，当在学习算法的训练中使用模式形状时，温度变化下的损伤检测的有效性显着提高。特别是对于较小的损害，所提出的算法在区分系统变化方面表现更好。这项研究是首次使用PCA和AE进行无监督机器学习，以利用自然频率以外的模式形状进行有效的损伤检测。将该管道的检测性能与类似的方法进行了比较，在该方法中，其学习模型未利用模式形状。结果表明，当在学习算法的训练中使用模式形状时，温度变化下的损伤检测的有效性显着提高。特别是对于较小的损害，所提出的算法在区分系统变化方面表现更好。结果表明，当在学习算法的训练中使用模式形状时，温度变化下的损伤检测的有效性显着提高。特别是对于较小的损害，所提出的算法在区分系统变化方面表现更好。结果表明，当在学习算法的训练中使用模式形状时，温度变化下的损伤检测的有效性显着提高。特别是对于较小的损害，所提出的算法在区分系统变化方面表现更好。