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Outline
To address the difficulty of directly monitoring fatigue damage in bogie-end suspension components caused by vibration during long-term service, this study takes a track inspection device as an example to analyze its fatigue failure mechanism and proposes an indirect fatigue stress monitoring scheme based on deep learning. Hazardous measurement points were identified via finite element analysis, and data on vibration acceleration at the frame end and component stress were collected through field line tests. Three temporal regression models (RNN, LSTM, and TCN) were constructed to establish the mapping relationship between acceleration loads at the frame end and component stress. The attention mechanism was introduced to optimize model performance. Experimental results demonstrate that all three models effectively predict stress, with the LSTM model incorporating the attention mechanism (LSTM-Att) achieving the highest prediction accuracy. Validation on six stress measurement points of the track inspection device confirmed that LSTM-Att exhibits excellent predictive performance in both the time and frequency domains. This proves the effectiveness of the proposed method and offers a novel approach for monitoring fatigue damage in frame end-suspended components.
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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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