Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU
Abstract As an important indicator of vehicle systems, tire load is a key factor in the structural design and safety assessment of vehicles. Direct measurement methods for tire loads are expensive and complicated, while conventional load identification methods are limited by low accuracy and poor ro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-11-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00357-9 |
| _version_ | 1797451571006537728 |
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| author | Yuanjin Ji Junwei Zeng Lihui Ren |
| author_facet | Yuanjin Ji Junwei Zeng Lihui Ren |
| author_sort | Yuanjin Ji |
| collection | DOAJ |
| description | Abstract As an important indicator of vehicle systems, tire load is a key factor in the structural design and safety assessment of vehicles. Direct measurement methods for tire loads are expensive and complicated, while conventional load identification methods are limited by low accuracy and poor robustness. This study aimed to propose a radial load identification method for rubber-tired vehicles based on a one-dimensional convolutional neural network (1D CNN) and bidirectional gated recurrent unit (BiGRU). Considering a priori information of the radial load data of tires and based on the observability of the vehicle vibration system, the proposed method selected feature sets and then retained the effective feature subsets through feature selection to construct samples with multiple time steps as input and with a single time step as output for network training. In doing so, the load prediction results were obtained, and the theoretical model was modified by integrating prediction accuracy, generalization performance, and robustness. Compared with traditional algorithms, the proposed method could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field. |
| first_indexed | 2024-03-09T14:56:33Z |
| format | Article |
| id | doaj.art-d3cb3f80984a485eb1a4c95f490e8d74 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-09T14:56:33Z |
| publishDate | 2023-11-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-d3cb3f80984a485eb1a4c95f490e8d742023-11-26T14:11:54ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-11-0116111410.1007/s44196-023-00357-9Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRUYuanjin Ji0Junwei Zeng1Lihui Ren2Rail Transit Institute of Tongji UniversityRail Transit Institute of Tongji UniversityRail Transit Institute of Tongji UniversityAbstract As an important indicator of vehicle systems, tire load is a key factor in the structural design and safety assessment of vehicles. Direct measurement methods for tire loads are expensive and complicated, while conventional load identification methods are limited by low accuracy and poor robustness. This study aimed to propose a radial load identification method for rubber-tired vehicles based on a one-dimensional convolutional neural network (1D CNN) and bidirectional gated recurrent unit (BiGRU). Considering a priori information of the radial load data of tires and based on the observability of the vehicle vibration system, the proposed method selected feature sets and then retained the effective feature subsets through feature selection to construct samples with multiple time steps as input and with a single time step as output for network training. In doing so, the load prediction results were obtained, and the theoretical model was modified by integrating prediction accuracy, generalization performance, and robustness. Compared with traditional algorithms, the proposed method could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.https://doi.org/10.1007/s44196-023-00357-9Bidirectional gated recurrent unit (BiGRU)Load identificationOne-dimensional convolutional neural network (1D CNN)Rubber-tired vehicleVehicle system dynamics |
| spellingShingle | Yuanjin Ji Junwei Zeng Lihui Ren Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU International Journal of Computational Intelligence Systems Bidirectional gated recurrent unit (BiGRU) Load identification One-dimensional convolutional neural network (1D CNN) Rubber-tired vehicle Vehicle system dynamics |
| title | Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU |
| title_full | Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU |
| title_fullStr | Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU |
| title_full_unstemmed | Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU |
| title_short | Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU |
| title_sort | research on the prediction of tire radial load based on 1d cnn and bigru |
| topic | Bidirectional gated recurrent unit (BiGRU) Load identification One-dimensional convolutional neural network (1D CNN) Rubber-tired vehicle Vehicle system dynamics |
| url | https://doi.org/10.1007/s44196-023-00357-9 |
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