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In order to address the issue of longterm dependence caused by the extended time span of wheel wear data and improve the prediction accuracy, an improved BiLSTM metro wheel wear prediction model is proposed by optimizing Bidirectional long shortterm memory network (BiLSTM) with Sparrow search algorithm (SSA). Firstly, the hyperparameters of the BiLSTM algorithm, such as the number of neurons, iteration count, input batch size, and learning rate, are optimized using the SSA. This optimization process is conducted within a specified range to obtain the optimal values of these hyperparameters. This optimization process aims to obtain the optimal parameter values. Subsequently, the SSABiLSTM network model is constructed using these optimal parameter values to predict and analyze wheel wear. Tread wear and flange wear are taken as the research objects, and the measured historical wear data of wheel No.1 of the metro’s carriage # 1 are used as inputs to metro and validate the model, and compare the prediction results with those of MLP, LSTM, BiLSTM and SSA-LSTM models. The results show that the improved bidirectional long short-term memory network model has higher wear prediction accuracy, and the mean absolute percentage error (MAPE) of tread wear is reduced by 13.28%, 10.32%, and 1.47%, and flange wear by 9.5%, 0.46%, and 0.02%. The wear of the No. 1 wheel of the same metro No.2 and No.4 cars is predicted and compared with the measured wear data. The average absolute percentage error of tread wear is 1.34% and 1.42%, respectively, and the average absolute percentage error of rim wear is 0.18% and 0.19%, respectively. The results confirm that the model exhibits strong generalization capabilities. The wheel wear prediction model based on improved BiLSTM network (SSA-Bi-LSTM) has high prediction accuracy and good generalization, which provides theoretical support for the intelligent management of metro wheelsets and prolongs wheel service life.
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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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