  We propose a method, a model, and a form of presenting model results for
condition monitoring of a small set of wind turbines with rare failures. The
main new ingredient of the method is to sample failure thresholds according to
the profit they give to an operating company. The model is a multiple linear
regression with seasonal components and external regressors, representing all
sensor components except for the considered one. To overcome the scarcity of
the training data, we use the median sensor values from all available turbines
in their healthy state. The cumulated deviation from the normal behavior model
obtained for this median turbine is calibrated for each turbine at the
beginning of the test period and after known failures. The proposed form of
presenting results is to set a scale for possible costs, control for random
maintenance, and show a whole distribution of costs depending on the free model
parameters. We make a case study on an open dataset with SCADA data from
multiple sensors and show that considering the influence of turbine components
is more critical than seasonality. The distribution, the average, and the
standard deviation of maintenance costs can be very different for similar
minimal costs. Random maintenance can be more profitable than reactive
maintenance and other approaches. Our predictive maintenance model outperforms
random maintenance and competitors for the whole set of considered turbines,
giving substantial savings.
