Automatic Identification of Main Bearing Faults in Wind Turbine Based on Wavelet and Empirical Mode Decomposition
The automatic identification nodes of main bearing fault of conventional wind turbine are mostly set as independent forms,and the automatic identification range is limited,resulting to the increase of misidentification rate.Therefore,the design of automatic identification method of main bearing fault of wind turbine based on wavelet and empirical mode decomposition is proposed.According to the current test,adopt the way of multi-target,break the limit of automatic identification range,multi-target fault monitoring node deployment,based on this,extract the main bearing fault vibration signal characteristics,and build the wavelet+experience mode decomposition generator set main bearing fault automatic identification model,using the interactive mark to realize automatic fault identification processing.The results show that the misidentification rate obtained by the designed wavelet and empirical mode decomposition wind turbine main bearing fault automatic identification test group is well controlled below 10%,indicating that with the assistance of wavelet and empirical mode decomposition technology,the designed main bearing fault automatic identification method is more efficient,targeted,and has practical application value.
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