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By predicting the wear trend of aeroengine, the wear state of aeroengine can be monitored effectively. Among the effective observation data reflecting the engine wear state, the oil analysis data can indirectly reflect the overall wear trend of aeroengine. Therefore, by establishing a trend prediction model based on oil sample analysis data, so as to realize the wear trend prediction of engine. However, the current models used in aeroengine trend prediction are mainly single prediction models, and the combined prediction models are only general linear combinations, with poor prediction effect. Therefore, a nonlinear variable weight combination prediction model based on support vector machine is proposed, and realizes the parameter optimization through particle swarm optimization algorithm. The oil sample analysis data is obtained through the bearing fatigue test of the whole life oil system, and the oil samples are collected at fixed intervals for performance analysis. Through the combination prediction analysis of the spectral analysis data, by comparing the prediction results of the combination prediction and the prediction results of the single prediction model, the prediction accuracy exceeds the prediction accuracy of the single prediction model, which fully verifies the superiority and effectiveness of the combination prediction model proposed in this paper.
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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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