疲劳损伤预测中数据稀缺的自监督学习
Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem
摘要：
随着预测和健康管理（PHM）数据的可用性越来越高，深度学习（DL）技术现在成为该应用程序的关注主题，通常可以实现更准确的剩余使用寿命（RUL）预测。然而，DL技术的主要挑战之一在于难以在工业系统上获得大量的标记数据。为了克服这种缺乏标记数据的情况，我们在工作中考虑了一种新兴的学习技术：自监督学习，这是无监督学习方法的一个子类。本文旨在研究在未标记的传感器数据上以自监督的方式预训练DL模型是否可以用于仅使用少镜头学习（即具有稀缺标记数据）的RUL估计。在这项研究中，一个疲劳损伤力学问题的解决，通过估计的RUL的铝合金板（典型的航空航天结构）的疲劳裂纹应变片数据。使用由应变数据组成的合成数据集，允许广泛研究数据集大小对预测性能的影响。结果表明，自监督预训练模型在下游RUL预测任务中能够显著优于非预训练模型，并且计算费用更少，在只有有限的标记数据可用时，在预测任务中显示出有希望的结果。
英文摘要：
With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for RUL estimation with only Few-Shots Learning, i.e. with scarce labelled data. In this research, a fatigue damage prognostics problem is addressed, through the estimation of the RUL of aluminum alloy panels (typical of aerospace structures) subject to fatigue cracks from strain gauge data. Synthetic datasets composed of strain data are used allowing to extensively investigate the influence of the dataset size on the predictive performance. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task, and with less computational expense, showing promising results in prognostic tasks when only limited labelled data is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
