论文标题
学习使用基于无人机的检查图像来识别风力涡轮机叶片表面的裂缝
Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images
论文作者
论文摘要
预计风能将成为实现《巴黎协议》目标的主要方式之一，但进而在很大程度上取决于其运营和维护（O＆M）成本的有效管理。刀片故障占所有O＆M成本的三分之一，因此可以准确检测刀片损坏，尤其是破裂，对于持续操作和节省成本非常重要。传统上，损坏检查是一个完全手动的过程，因此使其主观，容易出错且耗时。因此，在这项工作中，我们在使用深度学习的损害检查过程中带来了更大的客观性，可扩展性和可重复性，以减少裂缝。我们建立了一个深度学习模型，该模型在我们的基于无人机检查的大型刀片损坏数据集中训练，以正确检测裂缝。我们的模型已经在生产中，并且已经处理了超过100万赔偿，召回0.96。我们还专注于使用类激活图的模型可解释性，以窥视模型工作。该模型不仅表现出色，而且在某些棘手的情况下也更好。因此，在这项工作中，我们旨在通过减少其主要障碍之一来增加风能的采用 - o \＆m由于缺少刀片故障（如裂缝）而产生的O \＆M。
Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operations and cost savings. Traditionally, damage inspection has been a completely manual process thus making it subjective, error-prone, and time-consuming. Hence in this work, we bring more objectivity, scalability, and repeatability in our damage inspection process, using deep learning, to miss fewer cracks. We build a deep learning model trained on a large dataset of blade damages, collected by our drone-based inspection, to correctly detect cracks. Our model is already in production and has processed more than a million damages with a recall of 0.96. We also focus on model interpretability using class activation maps to get a peek into the model workings. The model not only performs as good as human experts but also better in certain tricky cases. Thus, in this work, we aim to increase wind energy adoption by decreasing one of its major hurdles - the O\&M costs resulting from missing blade failures like cracks.
