Multi-task distribution learning approach to anomaly detection of operational states of wind turbines

Abstract

The detection of abnormal operation modes is of fundamental importance for both operational management and predictive maintenance of wind turbines. Anomaly detection approaches in this context should consider the additional information content that probabilistic models can provide. Instead of binary anomaly classification, the probabilistic information is necessary for proper decision making and risk assessment. Common models, such as quantile and distribution regression can provide probabilistic information. While they are appropriate in predicting the cumulative distribution function, they struggle to accurately describe the probability of an event to occur. In this article we present a new, multi-task learning based approach for a continuous distribution regression with deep neural networks. Using real-world data from an offshore wind turbine, we show that with this model we can better reflect the probability of observed events than with conventional methods. While the predicted cumulative distribution function has a similar quality and no significant differences are visible in the continuous ranked probability score, the probability density function will be substantially smoother. This is also reflected in a significantly lower ignorance score.