Abstract
The wheel force data acquisition system for six-component force measurements often encounters noise signals during the data acquisition process. If these noises are not removed, they can severely impact the accuracy of vehicle performance analysis. This paper proposes a noise reduction method (IWOA-VMD-IWT) based on an improved whale optimization algorithm combined with variational mode decomposition and improved wavelet thresholding. By using IWOA to optimize the VMD parameters, the sensor signal is decomposed into intrinsic mode functions (IMFs). These IMFs are then classified into noise-dominant and signal-dominant components based on similarity results. The noise-dominant components undergo improved wavelet thresholding, and the noise-reduced signal is obtained through final reconstruction. Experimental results indicate that the SNR exceeds 14 dB and the RMSE is below 0.17. This represents an improvement of over 8 dB in SNR and more than 0.14 in RMSE compared to traditional wavelet threshold methods. The findings demonstrate the feasibility of the proposed noise reduction method in vehicle motion analysis.
Qiang Li
Professor and header of AG
My research interests include underwater robot, collaborative robots, humanoid robots.