CNN-BLADENET-all code

This paper proposes a new method for WTB condition monitoring based on a CNN named "BladeNet". First, the model structure is defined using a CNN for optical flow estimation. Simultaneously, a speckle dataset is constructed to optimize the model through training. Subsequently, the performance of the proposed CNNs is evaluated using the star displacement field. Finally, the proposed CNN model is validated with authentic WTB images. The experimental results suggest that the proposed method demonstrates enhanced feasibility for practical wind energy applications, potentially advancing the adoption of clean energy.本文提出了一种基于名为BladeNet的卷积神经网络（Convolutional Neural Network, CNN）的风力涡轮机叶片（Wind Turbine Blade, WTB）状态监测新方法。首先，针对光流估计任务，基于卷积神经网络搭建模型架构；与此同时，构建散斑数据集以通过训练优化该模型。随后，采用星型位移场对所提出的卷积神经网络的性能开展评估；最后，使用真实的WTB图像对所提出的卷积神经网络模型进行验证。实验结果表明，所提方法在实际风能应用场景中具备更优异的可行性，有望推动清洁能源的普及应用。

搜集汇总

背景与挑战

背景概述

该数据集基于CNN方法'BladeNet'，用于风力涡轮机叶片状态监测，通过光流估计和散斑数据集优化模型，实验验证显示其在风能应用中具有可行性，有助于推动清洁能源发展。数据集的核心是支持WTB监测的深度学习模型代码和相关数据。