Ice is the enemy of turbines everywhere. Some wind farms report energy production losses of up to 20 percent due to icing, according to Canadian wind-industry consultancy firm TechnoCentre Éolien (TCE), and that's not the worst of it. Over time, ice shedding from blades can damage other blades or overstress internal components, necessitating costly repairs. There's a clear and present use case, then, for an AI system that detects wind turbine icing. Fortunately, that's just what a team of researchers recently described in a paper published on the preprint server Arxiv.org

Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. We effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.