  The wind energy industry has been experiencing tremendous growth and
confronting the failures of wind turbine components. Wind turbine gearbox
malfunctions are particularly prevalent and lead to the most prolonged downtime
and highest cost. This paper presents a data-driven gearbox fault detection
algorithm base on high frequency vibration data using graph neural network
(GNN) models and sparse filtering (SF). The approach can take advantage of the
comprehensive data sources and the complicated sensing networks. The GNN
models, including basic graph neural networks, gated graph neural networks, and
gated graph sequential neural networks, are used to detect gearbox condition
from knowledge-based graphs formed using wind turbine information. Sparse
filtering is used as an unsupervised feature learning method to accelerate the
training of the GNN models. The effectiveness of the proposed method was
verified on practical experimental data.
