Autoencoder-based Anomaly Root Cause Analysis for Wind Turbines

Abstract A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder. These models have proven to be very successful in detecting such deviations, yet cannot show the underlying cause or failure directly. Such information is necessary for the implementation of these models in the planning of maintenance actions. In this paper we introduce a novel method: ARCANA. We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder. It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably. This reconstruction must be similar to the anomaly and thus identify only a few, but highly explanatory anomalous features, in the sense of Ockham’s razor. The proposed method is applied on an open data set of wind turbine sensor data, where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment. The results are compared with the reconstruction errors of the autoencoder output. The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features, whereas using the non-optimised reconstruction error does not. Even though the deviation in one specific input feature is very large, the reconstruction error of many other features is large as well, complicating the interpretation of the detected anomaly. Additionally, we apply ARCANA to a set of offshore wind turbine data. Two case studies are discussed, demonstrating the technical relevance of ARCANA.