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Focusing on the main working phase of liquid rocket engine, with the aid of multivariate non-linear time series analysis, and based on Dual Stage Attention Based Recurrent Neural Networks (DA-RNN), a new time series analysis tool, Convolutional Dual Stage Attention Based Recurrent Neural Networks (CDA-RNN), is proposed, by which a fault trend prediction model is established. Compared with LSTM, DA-RNN, etc, this model shows higher prediction accuracy. Combined with autocorrelation analysis of the prediction residual, a quantitative basis of fault detection is proposed after introducing failure confidence probability. Using hot test data with weak fault to validate the model, result shows that the CDA-RNN model enables robust weak fault muti-parameter detection in unsteady working process. This strategy is so effective that it calls for direct engineering application.
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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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