Identification of vehicle suspension shock absorber rattle noise based on wavelet packet feature fusion and GWO-LSTM

Jie Hou; Hongwei Yi; Xingyu Xiang; Xiangyu Ni; Ruxue Dai; Xiaorong Huang

doi:10.59400/sv1941

Identification of vehicle suspension shock absorber rattle noise based on wavelet packet feature fusion and GWO-LSTM

Jie Hou Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China; Sichuan Engineering Research Center of Intelligent Control and Simulation Test Technology for New Energy Vehicles, Xihua University, Chengdu 610039, China
Hongwei Yi Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China; Sichuan Engineering Research Center of Intelligent Control and Simulation Test Technology for New Energy Vehicles, Xihua University, Chengdu 610039, China
Xingyu Xiang Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China; Sichuan Engineering Research Center of Intelligent Control and Simulation Test Technology for New Energy Vehicles, Xihua University, Chengdu 610039, China
Xiangyu Ni State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery, Xuzhou Construction Machinery Group, Xuzhou 211162, China
Ruxue Dai School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Xiaorong Huang Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China; Sichuan Engineering Research Center of Intelligent Control and Simulation Test Technology for New Energy Vehicles, Xihua University, Chengdu 610039, China

Article ID: 1941

DOI: https://doi.org/10.59400/sv1941

Keywords: wavelet packet energy (WPE); wavelet packet fuzzy entropy (WPFE); grey wolf optimizer (GWO); long short-term memory (LSTM)

Abstract

With the advancement of pure electric vehicles, the issue of rattle noise in suspension shock absorbers has increasingly become a critical factor affecting vehicle comfort. This paper proposes a method for rattle noise recognition based on wavelet packet feature fusion and the grey wolf optimizer-long short-term memory (GWO-LSTM) model, aimed at improving the accuracy and efficiency of rattle noise detection. The vibration signals of the shock absorbers are decomposed by wavelet packet decomposition (WPD), followed by extraction of wavelet packet energy (WPE) and wavelet packet fuzzy entropy (WPFE) features and feature fusion. Subsequently, the GWO algorithm is employed to optimize the hyperparameters of the LSTM model, enhancing classification performance. The results demonstrate that, compared to traditional methods, the GWO-LSTM model significantly improves classification accuracy and training efficiency, achieving an accuracy rate of 97.85%, particularly excelling in the recognition of both slight and serious rattle noise. This study provides an efficient and reliable solution for the automated evaluation of shock absorbers’ rattle noise.

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Published

2025-03-14

How to Cite

Hou, J., Yi, H., Xiang, X., Ni, X., Dai, R., & Huang, X. (2025). Identification of vehicle suspension shock absorber rattle noise based on wavelet packet feature fusion and GWO-LSTM. Sound & Vibration, 59(2), 1941. https://doi.org/10.59400/sv1941

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Identification of vehicle suspension shock absorber rattle noise based on wavelet packet feature fusion and GWO-LSTM

Abstract

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