  The main objective of this paper is finding effective gearbox condition
monitoring methods by using continuously recorded monitoring SCADA (Supervisory
Control and Data Accusation) data points. Typically for wind turbine gearbox
condition monitoring; temperature readings, high frequency sounds and
vibrations in addition to lubricant condition monitoring have been used.
However, collection of such data, require shutting down equipment for
installation of costly sensors and measuring lubricant quality. Meanwhile,
operational data usually collected every 10 minutes, comprised of wind speed,
power generated, pitch angle and similar performance parameters can be used for
monitoring health of wind turbine components such as blades, gearbox and
generator. This paper uses gear rotational speed for monitoring health of
gearbox teeth; since gearbox teeth deterioration can be measured by monitoring
rotor to generator rotation ratios over extended period of time. As nature of
wind is turbulent with rapid fluctuations, a wind turbine may operate in
variety of modes within relatively short period of time. Monitoring rotational
speed ratio over time, requires consistent operational conditions such as wind
speed and torques within the gearbox. This paper also introduces the concept of
clustering such as Normal Mixture algorithm for dividing operating datasets
into consistent subgroups, which are used for long term monitoring.
