Archives of Acoustics, 47, 1, pp. 43–55, 2022

A Rattle Signal Denoising and Enhancing Method Based on Wavelet Packet Decomposition and Mathematical Morphology Filter for Vehicle

Linyuan LIANG
State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122; State Key Laboratory of Automotive Simulation and Control, Jilin University

Shuming CHEN
State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122; State Key Laboratory of Automotive Simulation and Control, Jilin University

Peiran LI
State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122

Buzz, squeak and rattle (BSR) noise has become apparent in vehicles due to the significant reductions in engine noise and road noise. The BSR often occurs in driving condition with many interference signals. Thus, the automatic BSR detection remains a challenge for vehicle engineers. In this paper, a rattle signal denoising and enhancing method is proposed to extract the rattle components from in-vehicle background noise. The proposed method combines the advantages of wavelet packet decomposition and mathematical morphology filter. The critical frequency band and the information entropy are introduced to improve the wavelet packet threshold denoising method. A rattle component enhancing method based on multi-scale compound morphological filter is proposed, and the kurtosis values are introduced to determine the best parameters of the filter. To examine the feasibility of the proposed algorithm, synthetic brake caliper rattle signals with various SNR ratios are prepared to verify the algorithm. In the validation analysis, the proposed method can well remove the disturbance background noise in the signal and extract the rattle components with well SNR ratios. It is believed that the algorithm discussed in this paper can be further applied to facilitate the detection of the vehicle rattle noise in industry.
Keywords: rattle signals; wavelet packet decomposition; mathematical morphology filter; critical frequency band; information entropy.
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DOI: 10.24425/aoa.2022.140731