Transfer learning-based fault detection in wind turbine blades using radar plots and deep learning models

Faults in wind turbine blades are considered a critical issue that can affect the safety and performance of wind turbines. The proposed research aimed to monitor wind turbine blades and identify fault conditions using a transfer learning approach. The study utilized one good and four faulty blade conditions: bend, hub-blade loose connection, erosion, and pitch angle twist. Vibration signals for each blade condition were collected and converted as radar plots that were fed and analyzed using pre-trained deep learning models including ResNet-50, AlexNet, VGG-16, and GoogleNet. Hyperparameters including optimizer, train-test split ratio, batch size, epochs, and learning rate were examined to determine the optimal configuration for each network. The study’s core findings indicate that ResNet-50 outperformed all other models, achieving an impressive accuracy rate of 99.00%. The other models achieved lower accuracy rates, with AlexNet achieving 96.70%, GoogleNet achieving 97.00%, and VGG-16 achieving 95.00%. These findings highlight the potential of using deep learning models for wind turbine monitoring and fault detection, which could significantly improve the efficiency and reliability of wind turbines.

风力发电机叶片故障是影响风力发电机组安全与运行性能的关键问题。本研究旨在通过迁移学习(transfer learning)方法对风力发电机叶片进行监测并识别故障状态。本研究选取了1种正常叶片状态与4种故障叶片状态：叶片弯曲、轮毂-叶片连接松动、叶片侵蚀以及桨距角扭转。研究采集了各叶片状态下的振动信号，并将其转换为雷达图谱，随后使用包括ResNet-50、AlexNet、VGG-16及GoogleNet在内的预训练深度学习模型对图谱进行训练与分析。本研究对优化器、训练测试划分比例、批量大小、训练轮次(epochs)及学习率等超参数进行了调试，以确定各网络的最优配置。本研究核心结果显示，ResNet-50的性能优于其余所有模型，准确率达到了令人瞩目的99.00%。其余模型的准确率相对较低：AlexNet为96.70%，GoogleNet为97.00%，VGG-16为95.00%。上述研究结果凸显了深度学习模型在风力发电机监测与故障检测领域的应用潜力，该技术有望显著提升风力发电机组的运行效率与可靠性。