DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms

Condition monitoring of critical components in wind turbines is of utmost importance to enable low cost and preventive maintenance and to minimize downtime. In this paper we propose DEEPWIND, an end-to-end condition monitoring and fault detection framework that can be implemented on resource-constrained embedded platforms. DEEPWIND exploits multi-channel convolutional neural networks to automatically extract features from sensor data without any need of human feature engineering, and utilizes the features to classify faults occurring in rotor blades of wind turbines. Experiments on a real-world dataset provided by Weidmüller Monitoring Systems GmbH reveal an average F1-score of 0.94 and thus underline the suitability of the approach for fault detection in wind turbines.