Vibration-Based Anomaly Detection Using FLAC Features for Wind Turbine Condition Monitoring

We present a method for detecting anomalies in vibration signals of wind turbine components. The predominant characteristics of wind turbine vibration signals are extracted by applying a time-frequency feature extraction method based on Fourier local autocorrelation (FLAC) features. For anomaly detection, one-class classification based on an unsupervised clustering approach is applied in consideration of the wind turbine’s dynamic operating conditions and environment. To validate the proposed system, we conducted experiments using the vibration data of actual 2 MW wind turbines. The results showed the effectiveness of using the FLAC features, particularly in the case of the low-speed main bearing where the conventional method with traditional features cannot detect the anomalies.