Fault Detection of Rolling Bearing of Wind Turbine Generator Based on GRU-LightGBM
Under the influence of various factors such as complex work environments and redundant load conditions,the frequency of rolling bearing failures remains high,posing a threat to the safety and reliability of wind turbine generator operation.Therefore,a fault detection method for wind turbine generator rolling bearings based on GRU-LightGBM was proposed.Effectively integrating the GRU model with the LightGBM algorithm,a rolling fault detection architecture for wind turbine generators was established.Based on the GRU model(update gate and reset gate),rolling bearing vibration data and electrical data features were processed.The speed data was selected and merged using weak and strong learners in the LightGBM algo-rithm to obtain load data feature fusion results.The rolling bearing fault feature set was loaded,and the cor-relation coefficient between the two was calculated.Based on this,the rolling bearing fault detection results were obtained.The experimental results showed that the maximum parallel processing efficiency of the rolling bearing speed data obtained by the proposed method reached 96MB/min,and the fusion of load data features tended to be consistent with the actual results.The fault detection results of rolling bearings were consistent with the actual results,fully confirming that the proposed method has better fault detection performance for rolling bearings.
wind turbinerolling bearingGRU-LightGBMgeneratorfault detectionfeature extraction of running data
万方数据
维普
