Wind turbine fault prediction using soft label SVM

Abstract

In this paper, we address the problem of predicting wind turbine electrical subsystem fault using time series data obtained from multiple sensors on wind turbine. While considering this as a time series classification problem, we are facing with the challenge that there is no explicit label information regarding the temporal location and duration of symptoms of the fault. Besides, significant data variation caused by both external and internal factors make the identification of change point non-trivial. To address these challenges, we propose a soft label SVM method where the probability of fault instead of binary label is used to train classifier to handle the uncertainty in label information. The probability is determined using temporal information of fault instances. We consider this as a weakly supervised learning problem. To handle large variation within data, we perform customized normalization on different sensor data based on their physical meanings and relationships. Finally, we evaluate our method on 38 different forced outage instances. The experiment on real SCADA data obtained from wind turbines show promising results where we can predict the triggering of fault 18 hours beforehand with an average AUC value 0.91.