The proportion of worldwide installed wind power in power systems increases over the years as a result of the steadily
growing interest in renewable energy sources. Still, the advantages offered by the use of wind power are overshadowed
by the high operational and maintenance costs, resulting in the low competitiveness of wind power in the energy market.
In order to reduce the costs of corrective maintenance, the application of condition monitoring to gearboxes becomes
highly important, since gearboxes are among the wind turbine components with the most frequent failure observations.
While condition monitoring of gearboxes in general is common practice, with various methods having been developed
over the last few decades, wind turbine gearbox condition monitoring faces a major challenge: the detection of faults
under the time-varying load conditions prevailing in wind turbine systems. Classical time and frequency domain methods
fail to detect faults under variable load conditions, due to the temporary effect that these faults have on vibration signals.
This paper uses the statistical discipline of outlier analysis for the damage detection of gearbox tooth faults. A simplified
two-degree-of-freedom gearbox model considering nonlinear backlash, time-periodic mesh stiffness and static
transmission error, simulates the vibration signals to be analysed. Local stiffness reduction is used for the simulation of
tooth faults and statistical processes determine the existence of intermittencies. The lowest level of fault detection, the
threshold value, is considered and the Mahalanobis squared-distance is calculated for the novelty detection problem.