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1Research and Development Institute of China First Automobile Group Co., Ltd, Changchun, China
2State Key Laboratory of Comprehensive Technology for Automobile Vibration Noise and Safety Control, Changchun, China
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.
Working Characteristics of Shock Absorber, Deep Learning Algorithm, LSTM, Dynamic Response Prediction
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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