A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data
This addresses maintenance scheduling for wind turbine operators, but it appears incremental as it applies existing machine learning and statistical tools to a specific domain.
The paper tackles predictive maintenance for wind turbine components using SCADA data, achieving anomaly prediction up to 2 months before unscheduled downtime in offline tests and confirming detection capabilities in a 12-month real-time test.
In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 283 MW. The results demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Furthermore, the real-time 12-months test confirms the ability of the proposed system to detect several anomalies, therefore allowing the operators to identify the root causes, and to schedule maintenance actions before reaching a catastrophic stage.