Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning

Jinyun Chang; Xuewu Zhu; Chao Han; Xingming Zhao; Jiaxing Sun; Feng Hu

doi:doi:10.11648/j.ajece.20220602.15

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Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning

Jinyun Chang, Xuewu Zhu, Chao Han, Xingming Zhao, Jiaxing Sun, Feng Hu

Published in American Journal of Electrical and Computer Engineering (Volume 6, Issue 2)

Received: 25 November 2022 Accepted: 16 December 2022 Published: 29 December 2022

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Abstract

The shock absorber is an important component of the automobile suspension system, which mainly plays the role of attenuating vibration during the driving of the car. The shock absorber is subjected to complex alternating loads during the recovery and compression process, and its dynamic damping characteristics show strong nonlinearity. The dynamic performance of the shock absorber has an important impact on the vehicle ride comfort and handling stability, so it is of great significance to carry out the prediction research on the working characteristics of the shock absorber. This paper introduces the structure and working principle of an automobile hydraulic shock absorber, and analyzes the reasons for the high nonlinearity of the working characteristics of the shock absorber. A prediction method and implementation framework of shock absorber working characteristics based on long short memory neural network (LSTM) algorithm are proposed, and abundant sample data are obtained through passenger vehicle durability test and shock absorber bench test. The effectiveness of feature selection is verified by data preprocessing and distribution law statistics. Finally, the LSTM intelligent algorithm is used to train, verify and test the sample data, and a prediction model of the working characteristics of the shock absorber is established. By comparing with the actual working characteristics data of the shock absorber, the accuracy and applicability of the prediction model are verified.

Published in	American Journal of Electrical and Computer Engineering (Volume 6, Issue 2)
DOI	10.11648/j.ajece.20220602.15
Page(s)	91-98
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Working Characteristics of Shock Absorber, Deep Learning Algorithm, LSTM, Dynamic Response Prediction

References

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APA Style

Jinyun Chang, Xuewu Zhu, Chao Han, Xingming Zhao, Jiaxing Sun, et al. (2022). Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. American Journal of Electrical and Computer Engineering, 6(2), 91-98. https://doi.org/10.11648/j.ajece.20220602.15

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ACS Style

Jinyun Chang; Xuewu Zhu; Chao Han; Xingming Zhao; Jiaxing Sun, et al. Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. Am. J. Electr. Comput. Eng. 2022, 6(2), 91-98. doi: 10.11648/j.ajece.20220602.15

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AMA Style

Jinyun Chang, Xuewu Zhu, Chao Han, Xingming Zhao, Jiaxing Sun, et al. Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. Am J Electr Comput Eng. 2022;6(2):91-98. doi: 10.11648/j.ajece.20220602.15

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@article{10.11648/j.ajece.20220602.15,
  author = {Jinyun Chang and Xuewu Zhu and Chao Han and Xingming Zhao and Jiaxing Sun and Feng Hu},
  title = {Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {2},
  pages = {91-98},
  doi = {10.11648/j.ajece.20220602.15},
  url = {https://doi.org/10.11648/j.ajece.20220602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220602.15},
  abstract = {The shock absorber is an important component of the automobile suspension system, which mainly plays the role of attenuating vibration during the driving of the car. The shock absorber is subjected to complex alternating loads during the recovery and compression process, and its dynamic damping characteristics show strong nonlinearity. The dynamic performance of the shock absorber has an important impact on the vehicle ride comfort and handling stability, so it is of great significance to carry out the prediction research on the working characteristics of the shock absorber. This paper introduces the structure and working principle of an automobile hydraulic shock absorber, and analyzes the reasons for the high nonlinearity of the working characteristics of the shock absorber. A prediction method and implementation framework of shock absorber working characteristics based on long short memory neural network (LSTM) algorithm are proposed, and abundant sample data are obtained through passenger vehicle durability test and shock absorber bench test. The effectiveness of feature selection is verified by data preprocessing and distribution law statistics. Finally, the LSTM intelligent algorithm is used to train, verify and test the sample data, and a prediction model of the working characteristics of the shock absorber is established. By comparing with the actual working characteristics data of the shock absorber, the accuracy and applicability of the prediction model are verified.},
 year = {2022}
}

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TY  - JOUR
T1  - Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning
AU  - Jinyun Chang
AU  - Xuewu Zhu
AU  - Chao Han
AU  - Xingming Zhao
AU  - Jiaxing Sun
AU  - Feng Hu
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.ajece.20220602.15
DO  - 10.11648/j.ajece.20220602.15
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 91
EP  - 98
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20220602.15
AB  - The shock absorber is an important component of the automobile suspension system, which mainly plays the role of attenuating vibration during the driving of the car. The shock absorber is subjected to complex alternating loads during the recovery and compression process, and its dynamic damping characteristics show strong nonlinearity. The dynamic performance of the shock absorber has an important impact on the vehicle ride comfort and handling stability, so it is of great significance to carry out the prediction research on the working characteristics of the shock absorber. This paper introduces the structure and working principle of an automobile hydraulic shock absorber, and analyzes the reasons for the high nonlinearity of the working characteristics of the shock absorber. A prediction method and implementation framework of shock absorber working characteristics based on long short memory neural network (LSTM) algorithm are proposed, and abundant sample data are obtained through passenger vehicle durability test and shock absorber bench test. The effectiveness of feature selection is verified by data preprocessing and distribution law statistics. Finally, the LSTM intelligent algorithm is used to train, verify and test the sample data, and a prediction model of the working characteristics of the shock absorber is established. By comparing with the actual working characteristics data of the shock absorber, the accuracy and applicability of the prediction model are verified.
VL  - 6
IS  - 2
ER  -

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Author Information

Jinyun Chang

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
Xuewu Zhu

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
Chao Han

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
Xingming Zhao

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
Jiaxing Sun

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
Feng Hu

Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China

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Plain Text BibTeX RIS

APA Style

Jinyun Chang, Xuewu Zhu, Chao Han, Xingming Zhao, Jiaxing Sun, et al. (2022). Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. American Journal of Electrical and Computer Engineering, 6(2), 91-98. https://doi.org/10.11648/j.ajece.20220602.15

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ACS Style

Jinyun Chang; Xuewu Zhu; Chao Han; Xingming Zhao; Jiaxing Sun, et al. Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. Am. J. Electr. Comput. Eng. 2022, 6(2), 91-98. doi: 10.11648/j.ajece.20220602.15

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AMA Style

Jinyun Chang, Xuewu Zhu, Chao Han, Xingming Zhao, Jiaxing Sun, et al. Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning. Am J Electr Comput Eng. 2022;6(2):91-98. doi: 10.11648/j.ajece.20220602.15

Copy | Download

@article{10.11648/j.ajece.20220602.15,
  author = {Jinyun Chang and Xuewu Zhu and Chao Han and Xingming Zhao and Jiaxing Sun and Feng Hu},
  title = {Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {2},
  pages = {91-98},
  doi = {10.11648/j.ajece.20220602.15},
  url = {https://doi.org/10.11648/j.ajece.20220602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220602.15},
  abstract = {The shock absorber is an important component of the automobile suspension system, which mainly plays the role of attenuating vibration during the driving of the car. The shock absorber is subjected to complex alternating loads during the recovery and compression process, and its dynamic damping characteristics show strong nonlinearity. The dynamic performance of the shock absorber has an important impact on the vehicle ride comfort and handling stability, so it is of great significance to carry out the prediction research on the working characteristics of the shock absorber. This paper introduces the structure and working principle of an automobile hydraulic shock absorber, and analyzes the reasons for the high nonlinearity of the working characteristics of the shock absorber. A prediction method and implementation framework of shock absorber working characteristics based on long short memory neural network (LSTM) algorithm are proposed, and abundant sample data are obtained through passenger vehicle durability test and shock absorber bench test. The effectiveness of feature selection is verified by data preprocessing and distribution law statistics. Finally, the LSTM intelligent algorithm is used to train, verify and test the sample data, and a prediction model of the working characteristics of the shock absorber is established. By comparing with the actual working characteristics data of the shock absorber, the accuracy and applicability of the prediction model are verified.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Research on Working Characteristics Prediction of Passenger Vehicle Shock Absorber Based on Deep Learning
AU  - Jinyun Chang
AU  - Xuewu Zhu
AU  - Chao Han
AU  - Xingming Zhao
AU  - Jiaxing Sun
AU  - Feng Hu
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.ajece.20220602.15
DO  - 10.11648/j.ajece.20220602.15
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 91
EP  - 98
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20220602.15
AB  - The shock absorber is an important component of the automobile suspension system, which mainly plays the role of attenuating vibration during the driving of the car. The shock absorber is subjected to complex alternating loads during the recovery and compression process, and its dynamic damping characteristics show strong nonlinearity. The dynamic performance of the shock absorber has an important impact on the vehicle ride comfort and handling stability, so it is of great significance to carry out the prediction research on the working characteristics of the shock absorber. This paper introduces the structure and working principle of an automobile hydraulic shock absorber, and analyzes the reasons for the high nonlinearity of the working characteristics of the shock absorber. A prediction method and implementation framework of shock absorber working characteristics based on long short memory neural network (LSTM) algorithm are proposed, and abundant sample data are obtained through passenger vehicle durability test and shock absorber bench test. The effectiveness of feature selection is verified by data preprocessing and distribution law statistics. Finally, the LSTM intelligent algorithm is used to train, verify and test the sample data, and a prediction model of the working characteristics of the shock absorber is established. By comparing with the actual working characteristics data of the shock absorber, the accuracy and applicability of the prediction model are verified.
VL  - 6
IS  - 2
ER  -

Copy | Download