Time-Domain Signal Reconstruction of Vehicle Interior Noise Based on Data-Driven Method

Title:Time-Domain Signal Reconstruction of Vehicle Interior Noise Based on Data-Driven Method

Volume: 15 Issue: 1

Author(s): Jiaqi Cao, Xiaolan Wang*, Yansong Wang, Zhuo Li and Dongpo Yang

Affiliation:

• School of mechanical and automotive engineering, Shanghai University of Engineering Science, Shanghai 201600, P.R. China

Keywords: Data-driven, interior noise, radial basis function neural network, BP neural network, wavelet decomposition, reconstruction method.

Abstract:

Background: In the process of high-speed driving, there are many source signals that affect the ear noise of passengers in the car. It is important to obtain the reference signal of Active Noise Control (ANC) of the vehicle at high-speed conditions.

Objective: This paper introduces a method to study the time-domain signal reconstruction of interior noise based on a data-driven method.

Methods: Based on the noise signal collected in a car, the key point signals affecting the interior noise are determined by the acoustic transfer path analysis method. Considering the time-varying characteristics of the noise signal and the complex nonlinear relationship of interior noise, a noise reconstruction model based on wavelet decomposition Radial Basis Function (RBF) neural network is established. Furthermore, the BP neural network noise reconstruction model is set up to compare the reconstruction effect.

Results: According to the reconstruction comparison, the average absolute error (0.0072) of the proposed algorithm model is smaller than the average absolute error of the noise reconstruction BP network model based on wavelet decomposition (0.0280), and the accuracy is improved by 74.29%. The average absolute error between the reconstructed value of the RBF neural network and the real value is smaller than that of the BP neural network, and the error of the proposed model is less than 0.01. The method proposed in this paper can reconstruct the interior noise signal of the vehicle accurately and effectively.

Conclusion: This paper proposes a reconstruction model of vehicle interior noise signal based on wavelet decomposition RBF neural network algorithm driven by data-driven, and verifies the effectiveness of the algorithm with the real vehicle test data. The reconstruction method of RBF neural network based on data-driven wavelet decomposition provides a certain reference value for ANC to obtain a highprecision reference signal.

Cite this article as:

Cao Jiaqi , Wang Xiaolan *, Wang Yansong , Li Zhuo and Yang Dongpo , Time-Domain Signal Reconstruction of Vehicle Interior Noise Based on Data-Driven Method, Recent Patents on Mechanical Engineering 2022; 15 (1) . https://dx.doi.org/10.2174/2212797614666210315153927