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Aiming at the nonlinearity and non-stationary of vibration signals of hydropower units and the timeliness of prediction, this paper proposed a vibration prediction model of hydropower units based on VMD-CIMFs-TCN. Firstly, the VMD algorithm was used to decompose the vibration signal to obtain the IMFs component with the minimum signal distortion, which realizes the accurate decomposition of the vibration signal. Secondly, by calculating the power spectrum entropy and the permutation entropy of each IMF component, the aggregation of the IMF components was realized to reduce the computational load of the prediction model. Finally, the TCN network was used to realize the accurate prediction of CIMFs, and the final vibration signal prediction results were obtained by adding them. The analysis shows that this method shortens the time required for prediction on the premise of ensuring the prediction accuracy, and meets the timeliness of the prediction model.
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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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