Detection of gearbox bearing defects using electrical signature analysis for Doubly-fed wind generators

Abstract

Drivetrain failures may cause severe damage to the wind turbines. In the previous work, detection of failures in generator bearing and gearbox gears using electrical signature analysis (ESA) has been investigated. However, the detection of defects of bearings in the gearboxes has been a major gap. Bearings defects in gearboxes are believed to be one of the root causes of wind drivetrain failures. In this paper, a novel electrical signature analysis-based monitoring technique is proposed for monitoring gearbox bearing defects in wind turbines, which is the first ESA technique reported capable of detecting bearing defects in gearboxes. A novel electrical signature tool, i.e., electrical multi-phase imbalance separation technique, has been used to improve the signal-to-noise ratio in electrical signature analysis. The principle of gearbox bearing defect detection is presented in detail. The proposed approach is validated by experimental results obtained from a 25 HP wind drivetrain simulator, which is designed to simulate 1.5 MW wind turbines as well as in the field on 1.5MW wind turbines. The experimental results show that the proposed approach is capable of providing accurate detection of gearbox bearing failures at early stage. The proposed approach is cost effective with reliable detection of defects compared to existing techniques.