  Most wind turbines are remotely monitored 24/7 to allow for an early
detection of operation problems and developing damage. We present a new fault
detection method for vibration-monitored drivetrains that does not require any
feature engineering. Our method relies on a simple model architecture to enable
a straightforward implementation in practice. We propose to apply convolutional
autoencoders for identifying and extracting the most relevant features from the
half spectrum in an automated manner, saving time and effort. Thereby, a
spectral model of the normal vibration response is learnt for the monitored
component from past measurements. We demonstrate that the model can
successfully distinguish damaged from healthy components and detect a damaged
generator bearing and damaged gearbox parts from their vibration responses.
Using measurements from commercial wind turbines and a test rig, we show that
vibration-based fault detection in wind turbine drivetrains can be performed
without the usual upfront definition of spectral features. Another advantage of
the presented method is that the entire half spectrum is monitored instead of
the usual focus on monitoring individual frequencies and harmonics.
