Diagnostic Framework for Wind Turbine Gearboxes Using Machine Learning

Abstract

Operation and maintenance costs of wind turbines are highlydriven by gearbox failures, especially offshore were the logisticsof replacements are more demanding. It is therefore verycritical to foresee incipient gearbox faults before they becomecatastrophic failures. Wind turbine gearbox condition monitoringis usually performed using vibration signals comingfrom accelerometers installed on the gearbox surface. Thecurrent monitoring practice is a rule-based approach, wherealarms are activated based on thresholds. However, too muchmanual analysis may be required for some failure modes andthis can become quite challenging as the installed wind capacitygrows. Also, since false alarms have to be avoided,these thresholds are set quite high, resulting in late stage diagnosisof components. Given the fact there is a large amountof historic operating data with confirmed gearbox failure incidents,this paper proposes a framework that uses a machinelearning approach. Vibration signals are used from the gearboxsensors and processed in the frequency domain. Featuresare extracted from the processed signals based on the fault locationsand failure modes, using domain knowledge. Thesefeatures are used as inputs in a layer of pattern recognitionmodels that can determine a potential component fault locationand failure mode. The proposed framework is illustratedusing failure examples from operating offshore wind turbines.