Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning
The suspension parameters of heavy-duty freight trains can deviate from their initial design values due to material aging and performance degradation. While traditional multibody dynamics simulation models are usually designed for fixed working conditions, it is difficult for them to adequately anal...
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MDPI AG
2024-12-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/12/181 |
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| author | Changfan Zhang Yuxuan Wang Jing He |
| author_facet | Changfan Zhang Yuxuan Wang Jing He |
| author_sort | Changfan Zhang |
| collection | DOAJ |
| description | The suspension parameters of heavy-duty freight trains can deviate from their initial design values due to material aging and performance degradation. While traditional multibody dynamics simulation models are usually designed for fixed working conditions, it is difficult for them to adequately analyze the safety status of the vehicle–line system in actual operation. To address this issue, this research provides a suspension parameter estimation technique based on CNN-GRU. Firstly, a prototype C80 train was utilized to build a simulation model for multibody dynamics. Secondly, six key suspension parameters for wheel–rail force were selected using the Sobol global sensitivity analysis method. Then, a CNN-GRU proxy model was constructed, with the actually measured wheel–rail forces as a reference. By combining this approach with NSGA-II (Non-dominated Sorting Genetic Algorithm II), the key suspension parameters were calculated. Finally, the estimated parameter values were applied into the vehicle–line coupled multibody dynamical model and validated. The results show that, with the corrected dynamical model, the relative errors of the simulated wheel–rail force are reduced from 9.28%, 6.24% and 18.11% to 7%, 4.52% and 10.44%, corresponding to straight, curve, and long and steep uphill conditions, respectively. The wheel–rail force simulation’s precision is increased, indicating that the proposed method is effective in estimating the suspension parameters for heavy-duty freight trains. |
| format | Article |
| id | doaj-art-24c9a5cfa173474fb3a04e2f5088e7ea |
| institution | DOAJ |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-24c9a5cfa173474fb3a04e2f5088e7ea2025-08-20T02:50:52ZengMDPI AGBig Data and Cognitive Computing2504-22892024-12-0181218110.3390/bdcc8120181Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep LearningChangfan Zhang0Yuxuan Wang1Jing He2College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaThe suspension parameters of heavy-duty freight trains can deviate from their initial design values due to material aging and performance degradation. While traditional multibody dynamics simulation models are usually designed for fixed working conditions, it is difficult for them to adequately analyze the safety status of the vehicle–line system in actual operation. To address this issue, this research provides a suspension parameter estimation technique based on CNN-GRU. Firstly, a prototype C80 train was utilized to build a simulation model for multibody dynamics. Secondly, six key suspension parameters for wheel–rail force were selected using the Sobol global sensitivity analysis method. Then, a CNN-GRU proxy model was constructed, with the actually measured wheel–rail forces as a reference. By combining this approach with NSGA-II (Non-dominated Sorting Genetic Algorithm II), the key suspension parameters were calculated. Finally, the estimated parameter values were applied into the vehicle–line coupled multibody dynamical model and validated. The results show that, with the corrected dynamical model, the relative errors of the simulated wheel–rail force are reduced from 9.28%, 6.24% and 18.11% to 7%, 4.52% and 10.44%, corresponding to straight, curve, and long and steep uphill conditions, respectively. The wheel–rail force simulation’s precision is increased, indicating that the proposed method is effective in estimating the suspension parameters for heavy-duty freight trains.https://www.mdpi.com/2504-2289/8/12/181deep learningheavy-duty freight trainsmachine learningCNN-GRU modelparameter estimation |
| spellingShingle | Changfan Zhang Yuxuan Wang Jing He Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning Big Data and Cognitive Computing deep learning heavy-duty freight trains machine learning CNN-GRU model parameter estimation |
| title | Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning |
| title_full | Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning |
| title_fullStr | Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning |
| title_full_unstemmed | Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning |
| title_short | Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning |
| title_sort | suspension parameter estimation method for heavy duty freight trains based on deep learning |
| topic | deep learning heavy-duty freight trains machine learning CNN-GRU model parameter estimation |
| url | https://www.mdpi.com/2504-2289/8/12/181 |
| work_keys_str_mv | AT changfanzhang suspensionparameterestimationmethodforheavydutyfreighttrainsbasedondeeplearning AT yuxuanwang suspensionparameterestimationmethodforheavydutyfreighttrainsbasedondeeplearning AT jinghe suspensionparameterestimationmethodforheavydutyfreighttrainsbasedondeeplearning |