Transfer Learning Approaches for Wind Turbine Fault Detection using Deep Learning

Abstract

Wind park operators start to recognize the cost-effectiveness of intelligent maintenance solutions for wind turbines based on the readily available 10-minute SCADA data. In particular, recent advances have shown that deep learning algorithms can enhance the performance and robustness of fault detection algorithms which are fed with such SCADA data. In order to deploy deep learning fault detection algorithms, a large amount of historical data is needed. In case the data is not available for a certain turbine, training the algorithms becomes challenging. The common approaches in this case are referred to as transfer learning or domain adaptation methods, which attempt to allow the transfer of knowledge between different machines. In this paper we explore the main challenges of domain adaptation for fault detection based on wind turbine SCADA data. We focus on practical use cases, stemming from the commercial need to deploy fault detection algorithms for newly installed turbines, or turbines with little historical data under diverse operating conditions. We analyze different reasons for domain shifts between turbines, which require the development of new domain adaptation approaches beyond the ones familiar for other PHM applications, and present results for several of these challenging cases.