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In previous researches on intelligent fault diagnosis methods of the hydroelectric generating unit, the subjectivity of the artificial selection of the fault classification characteristics and the limitations of small sample data have important impacts on the accuracy of fault diagnosis results. To solve this problem, a CNN-SVM method for the fault diagnosis of the hydroelectric generating unit was proposed by combining with the feature extraction advantages of convolutional neural network (CNN) and the excellent ability of support vector machine (SVM) in processing small sample. In this method, the time-domain diagram of the vibration signal of the hydroelectric generating unit was used as the model input, and the CNN method was employed to extract the signal features. Then, the extracted feature vector was input to the SVM method to realize the final fault diagnosis of the unit. Finally, the advantages of the diagnosis method proposed in this paper were verified through a specific example analysis.
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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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