Wind turbine generator bearing fault diagnosing method based on deep joint adaptation network
A technology for fault diagnosis of wind turbines, applied in the field of wind power, can solve problems such as failure to use new fault diagnosis of bearings, and achieve the effect of reducing computational complexity
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[0054] The present invention will be further described below in conjunction with the drawings and embodiments.
[0055] Such as figure 1 As shown, a wind turbine bearing fault diagnosis method based on a deep joint adaptation network includes the following steps:
[0056] 1) Establish a diversified fusion database: Collect wind power system rolling bearing end acceleration data under different working conditions, and at the same time collect wind power system rolling bearing end acceleration data in real time. According to the characteristics of multi-source, heterogeneous, and large noise in the industrial data, all data are collected. After normalization, Fourier transform is used to remove the noise contained in the data, and the processed data under different working conditions are labeled and marked as Source Domain (SD). The labels include normal and inner circle faults. , Outer ring failure, and sphere failure. The real-time collected data after preprocessing is recorded as...
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