{"title":"结合迁移学习和数值建模,解决基于数据的 SHM 缺乏训练数据的问题","authors":"","doi":"10.1016/j.jsv.2024.118710","DOIUrl":null,"url":null,"abstract":"<div><p>Structural health monitoring (SHM) involves continuously surveilling the performance of structures to identify progressive damage or deterioration that might evolve over time. Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. 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Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. 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引用次数: 0
摘要
结构健康监测(SHM)是指对结构性能进行持续监测,以识别可能随时间推移而发生的渐进式损坏或劣化。最近,机器学习(ML)算法已成功应用于包括损伤检测在内的各种 SHM 应用中。然而,有监督的 ML 算法通常需要对结构的多种可能损坏状态进行标注数据,才能成功识别损坏。虽然收集低价值结构的此类数据可能是可行的,但获取飞机等昂贵结构的损坏数据却极具挑战性。本文通过将有限元(FE)模型与域自适应相结合,特别是转移分量分析(TCA)和联合域自适应(JDA),解决了数据不足的问题。所提出的方法在两个案例研究中得到了展示,一个是 Brake-Reuß 梁,其损坏情况与搭接接头的不同扭矩设置相对应;另一个是翼盒实验室结构,其损坏情况为锯切。人工神经网络 (ANN) 和 K-Nearest Neighbours (KNN) 形式的监督学习算法在应用域适应后根据 FE 数据进行训练,然后用实验数据进行测试。结果表明,尽管分类器在双级、三级、四级和五级等不同情况下的性能对训练阶段的选择很敏感,但使用 TCA 或 JDA 可以使用 FE 数据进行训练,并大大减少了使用昂贵的实验损坏数据进行训练的需要。这些结果可以为在关键和/或昂贵结构的 SHM 中更广泛地使用 ML 算法铺平道路。
Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM
Structural health monitoring (SHM) involves continuously surveilling the performance of structures to identify progressive damage or deterioration that might evolve over time. Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. These results can pave the way for a broader use of ML algorithms in SHM of critical and/or expensive structures.
期刊介绍:
The Journal of Sound and Vibration (JSV) is an independent journal devoted to the prompt publication of original papers, both theoretical and experimental, that provide new information on any aspect of sound or vibration. There is an emphasis on fundamental work that has potential for practical application.
JSV was founded and operates on the premise that the subject of sound and vibration requires a journal that publishes papers of a high technical standard across the various subdisciplines, thus facilitating awareness of techniques and discoveries in one area that may be applicable in others.