Data Science in Engineering, Volume 10 - Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
Data Science in Engineering, Volume 10 - Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
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工程中的数据科学，第 10 卷 - 第 41 届 IMAC 会议论文集，2023 年结构动力学会议暨博览会
DOI：
10.1007/978-3-031-34946-1_7
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Bee S
中科院分区：
文献类型：
--
作者：
Bee S
最近已经努力转向基于人口的结构健康监测(PBSHM)系统。PBSHM的一个潜在发展领域是多任务学习(MTL)的使用：不同于传统独立学习算法的算法。这里介绍了MTL的使用，“联合特征选择与套索”，以提供结构数据集的自动特征选择。分类任务是区分尾翼飞机的左右侧，以获取来自同一型号的两架飞机的样本。独立学习者的F1成绩很好，但工程洞察力很差，而MTL的结果是可以解释的，突出了结构上的差异，而不是实验设置上的差异。
There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is the use of multi-task learning (MTL): algorithms which differ from traditional independent learning algorithms. Presented here is the use of the MTL, “joint feature selection with LASSO”, to provide automatic feature selection for a structural dataset. The classification task is to differentiate between the port and starboard side of a tail-plane, for samples from two aircraft of the same model. The independent learner produced perfect F1 scores but had poor engineering insight, whereas the MTL results were interpretable, highlighting structural differences as opposed to differences in experimental set-up.