A two-stage diagnosis framework for wind turbine gearbox condition monitoring

Abstract

Advances in high-performance sensing technologies enable the development of wind turbine condition monitoring systems to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two-stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method with a graphical verification method to ensure diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study using the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully detected five faults out of a total of seven with accurate severity levels and without producing any false alarm in the blind analysis. The case study results indicate that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drivetrain systems based on system vibration characteristics.