Convolutional neural networks for wind turbine gearbox health monitoring

Between different sources of renewable energy, wind energy, as an economical source of electrical power, has undergone a pronounced thriving. However, wind turbines are exposed to catastrophic failures, which may bring about irrecoverable ramifications. Therefore, they necessarily need condition monitoring and fault detection systems. These systems aim to reduce the number of attempts operators are required to do through the use of smart software algorithms, which are able to understand and decide with no human involvement. The gearboxes are usually responsible for the WT breakdowns. In this paper, convolutional neural networks are employed to develop an intelligent data-based condition-monitoring algorithm to differentiate healthy and damaged conditions that are evaluated with the national renewable energy laboratory (NREL) GRC database on the WT gearbox. Since it is much easier for convolutional neural networks to extract clues from high dimensional data, time-domain signals are embodied as texture images. Results show that the proposed methodology by utilizing a 2-D convolutional neural network for binary classification is capable of classifying the NREL GRC database with 99.76% accuracy.