Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data

Energies

Christian Gück¹  Stephan Vogt¹  Andres Ortega¹  Marc-Alexander Lutz¹  Volker Berkhout¹  Stefan Faulstich¹  Urs Steinmetz²  Steffen Dienst³ 

[1] Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany;STEAG Energy Services GmbH, Rüttenscheider Str. 1-3, 45128 Essen, Germany;Trianel Windpark Borkum GmbH und Co. KG, Zirkusweg 2, 20359 Hamburg, Germany;

关键词: wind turbine;    maintenance;    autoencoder;    machine learning;    reliability;    data driven model;    service;    performance;   

DOI  :  10.3390/en13051063

来源: DOAJ

【 摘 要 】

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

【 授权许可】

Unknown