  A growing number of wind turbines are equipped with vibration measurement
systems to enable a close monitoring and early detection of developing fault
conditions. The vibration measurements are analyzed to continuously assess the
component health and prevent failures that can result in downtimes. This study
focuses on gearbox monitoring but is applicable also to other subsystems. The
current state-of-the-art gearbox fault diagnosis algorithms rely on statistical
or machine learning methods based on fault signatures that have been defined by
human analysts. This has multiple disadvantages. Defining the fault signatures
by human analysts is a time-intensive process that requires highly detailed
knowledge of the gearbox composition. This effort needs to be repeated for
every new turbine, so it does not scale well with the increasing number of
monitored turbines, especially in fast growing portfolios. Moreover, fault
signatures defined by human analysts can result in biased and imprecise
decision boundaries that lead to imprecise and uncertain fault diagnosis
decisions. We present a novel accurate fault diagnosis method for
vibration-monitored wind turbine components that overcomes these disadvantages.
Our approach combines autonomous data-driven learning of fault signatures and
health state classification based on convolutional neural networks and
isolation forests. We demonstrate its performance with vibration measurements
from two wind turbine gearboxes. Unlike the state-of-the-art methods, our
approach does not require gearbox-type specific diagnosis expertise and is not
restricted to predefined frequencies or spectral ranges but can monitor the
full spectrum at once.
