Operation and maintenance costs of wind turbines are highly
driven by gearbox failures, especially offshore were the logistics
of replacements are more demanding. It is therefore very
critical to foresee incipient gearbox faults before they become
catastrophic failures. Wind turbine gearbox condition monitoring
is usually performed using vibration signals coming
from accelerometers installed on the gearbox surface. The
current monitoring practice is a rule-based approach, where
alarms are activated based on thresholds. However, too much
manual analysis may be required for some failure modes and
this can become quite challenging as the installed wind capacity
grows. Also, since false alarms have to be avoided,
these thresholds are set quite high, resulting in late stage diagnosis
of components. Given the fact there is a large amount
of historic operating data with confirmed gearbox failure incidents,
this paper proposes a framework that uses a machine
learning approach. Vibration signals are used from the gearbox
sensors and processed in the frequency domain. Features
are extracted from the processed signals based on the fault locations
and failure modes, using domain knowledge. These
features are used as inputs in a layer of pattern recognition
models that can determine a potential component fault location
and failure mode. The proposed framework is illustrated
using failure examples from operating offshore wind turbines.
How to Cite
wind turbines, gearbox, vibrations
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