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In response to the challenge posed by the limited accuracy of traditional fault diagnosis methods in wind turbine gearbox applications due to the complex and variable operational conditions and the presence of significant noise, the MTFSwin Transformer wind turbine gearbox fault diagnosis model is proposed. Initially, the onedimensional vibration time series signal is transformed into a twodimensional feature map with correlated temporal information using the Markov Transition Field (MTF) graph encoding method. Subsequently, this feature map is employed as the input for the Swin Transformer model, which utilizes a selfattention mechanism for automatic feature extraction. This process culminates in the classification of various fault types. The results demonstrate a fault diagnosis accuracy of 99.48%, affirming the effectiveness and superiority of the proposed method.
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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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