Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.

Doppler-shifted WiFi signal components reflected from a moving target, though weak compared to the “direct” path signals, can be analyzed to find the Doppler. By combining the Doppler signals from multiple receivers, the Doppler can be used to determine the state - position and velocity - of a target moving in the vicinity of a WiFi transmitter. This paper provides a signal model of the received OFDM signal with Doppler, and describes how the Doppler can be extracted using compressed likelihood on channel state information based on spectral information extracted from the channel state information. The method accounts for the carrier frequency offset usually present. Experimental results are presented.