  A failure detection system is the first step towards predictive maintenance
strategies. A popular data-driven method to detect incipient failures and
anomalies is the training of normal behaviour models by applying a machine
learning technique like feed-forward neural networks (FFNN) or extreme learning
machines (ELM). However, the performance of any of these modelling techniques
can be deteriorated by the unexpected rise of non-stationarities in the dynamic
environment in which industrial assets operate. This unpredictable statistical
change in the measured variable is known as concept drift. In this article a
wind turbine maintenance case is presented, where non-stationarities of various
kinds can happen unexpectedly. Such concept drift events are desired to be
detected by means of statistical detectors and window-based approaches.
However, in real complex systems, concept drifts are not as clear and evident
as in artificially generated datasets. In order to evaluate the effectiveness
of current drift detectors and also to design an appropriate novel technique
for this specific industrial application, it is essential to dispose beforehand
of a characterization of the existent drifts. Under the lack of information in
this regard, a methodology for labelling concept drift events in the lifetime
of wind turbines is proposed. This methodology will facilitate the creation of
a drift database that will serve both as a training ground for concept drift
detectors and as a valuable information to enhance the knowledge about
maintenance of complex systems.
