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In order to improve the accuracy of intelligent diagnosis of reactor mechanical fault，according to the correlation characteristics between reactor vibration signal and mechanical state，a vibration diagnosis method of reactor mechanical fault based on stacked auto-encoder（SAE） was proposed. Firstly，the original vibration signal of reactor was decomposed by wavelet packet decomposition algorithm，and the time-frequency energy matrix of the signal was extracted. Then，the diagnosis model of reactor mechanical fault based on SAE was built，the deep feature mining of the time-frequency energy matrix was completed through unsupervised self-learning，and the identification of reactor mechanical fault was realized through supervised fine-tuning. Finally，vibration data of 10 kV oil immersed reactor under different mechanical states was used to train the fault identification model and optimize the super parameters. The numerical results show that the proposed method can identify reactor mechanical fault better than the traditional vibration signal identification method，and the accuracy can reach 98%.
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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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