A Lightweight CNN for Wind Turbine Blade Defect Detection Based on Spectrograms

Abstract

Since wind turbines are exposed to harsh working environments and variable weather conditions, wind turbine blade condition monitoring is critical to prevent unscheduled downtime and loss. Realizing that common convolutional neural networks are difficult to use in embedded devices, a lightweight convolutional neural network for wind turbine blades (WTBMobileNet) based on spectrograms is proposed, reducing computation and size with a high accuracy. Compared to baseline models, WTBMobileNet without data augmentation has an accuracy of 97.05%, a parameter of 0.315 million, and a computation of 0.423 giga floating point operations (GFLOPs), which is 9.4 times smaller and 2.7 times less computation than the best-performing model with only a 1.68% decrease in accuracy. Then, the impact of difference data augmentation is analyzed. The WTBMobileNet with augmentation has an accuracy of 98.1%, and the accuracy of each category is above 95%. Furthermore, the interpretability and transparency of WTBMobileNet are demonstrated through class activation mapping for reliable deployment. Finally, WTBMobileNet is explored in drones image classification and spectrogram object detection, whose accuracy and mAP@[0.5, 0.95] are 89.55% and 70.7%, respectively. This proves that WTBMobileNet not only has a good performance in spectrogram classification, but also has good application potential in drone image classification and spectrogram object detection.