Wind Turbine operational state prediction: towards featureless, end-to-end predictive maintenance

Traditionally, predictive maintenance of wind turbines has relied on experts to perform time consuming feature pre-processing using statistical, time and frequency domain analysis. Recent advancements in Convolutional Neural Networks have opened the potential for using featureless approaches that learn the discriminating patterns from big data sets without expert intervention. Given multi-dimensional time series data representing sensed electric currents, in this paper we explore the optimal window length that can be used to accurately determine its speed and load using Convolutional and Residual Networks. Choosing an optimal window length is a trade-off between accuracy and number of predictions per second. Fast predictions for operating parameters are useful in maintenance strategies but they come at a cost of decreased accuracy (as there is less data available in a shorter time interval). We show how fusing multiple signals can achieve high accuracy with nine speed and load cases varying from 375rpm with 0% load to 1500rpm and 100% load. Using Class Activation Maps we investigate features in the time domain picked up by the network to make its classification decisions. Further, we train the networks to predict the drive loads in a regression setting where the models are able to generalize well on unseen cases.