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• W3187105134 abstract "As a typical device of mechanical transmission, the speed-increasing gearbox of wind turbine is also the core component of wind turbine. Its working reliability directly affects the economic benefits of enterprises and the national economic output value. In the process of operation, it is necessary to strictly monitor its running status and diagnose faults in time to ensure the normal operation of equipment. For the operation and maintenance team of dozens of people, it can only maintain2Wind field about30Multiple devices, difficult operation and maintenance and low efficiency, operation and maintenance Dimension cost Increase. This paper will use the real-time monitoring data of wind turbine operation status and the daily maintenance data of wind turbine equipment, A fault classification method based on the combination of time domain synchronous data average and depth learning is established, and a fault diagnosis classification model of wind turbine bearings is built to verify the accuracy of the fault diagnosis classification method based on the combination of time domain synchronous average and depth learning on fault diagnosis results." @default.
• W3187105134 created "2021-08-16" @default.
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• W3187105134 date "2021-06-18" @default.
• W3187105134 modified "2023-09-23" @default.
• W3187105134 title "Research on Fault Diagnosis of Gearbox Bearing of Wind Turbine Generator Set Based on DNN-1.5 MW" @default.
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• W3187105134 doi "https://doi.org/10.1109/wsai51899.2021.9486366" @default.
• W3187105134 hasPublicationYear "2021" @default.
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