Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Sensors 2023
Mindaugas Jankauskas, Artūras Serackis, Martynas Šapurov, Raimondas Pomarnacki, Algridas Baskys, Van Khang Hyunh, Toomas Vaimann, Jānis Zaķis

The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection. © 2023 by the authors.

Atslēgas vārdi
anomaly; neural network; SCADA; temperature; wind turbine
DOI
10.3390/s23125695
Hipersaite
https://www.mdpi.com/1424-8220/23/12/5695

Jankauskas, M., Serackis, A., Šapurov, M., Pomarnacki, R., Baskys, A., Hyunh, V., Vaimann, T., Zaķis, J. Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique. Sensors, 2023, Vol. 23, No. 12, Article number 5695. ISSN 1424-3210. e-ISSN 1424-8220. Pieejams: doi:10.3390/s23125695

Publikācijas valoda
English (en)