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Aiming at the problem of the low fault diagnosis accuracy caused by the lack of fault samples for the rolling bearings of doubly fed wind turbines under normal conditions for a long time, an improved generative adversarial network fault diagnosis method based on expanding high-quality fault samples and using dual feature extraction was proposed. Firstly,a finite number of rolling bearing fault samples were expanded through a Wasserstein type generative adversarial network with maximum mean discrepancy and penalty constraints. Secondly, based on the dual feature extraction model, the time-frequency converted temporal features and local features were extracted separately. Finally, the fault diagnosis of the rolling bearing balance data was completed through a classifier. The standard dataset and test results show that the proposed method improves the fault diagnosis performance while lacking fault samples.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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