Application of the wavelet transforms for compressing lower suspension arm strain data

Abstract

This paper presents the ability of the wavelet transforms for compressing automobile strain data. The wavelet transforms identified and extracted higher amplitude segments and produced shorter edited signals. Based on the comparison of the edited signals resulted, it was found that the Morlet wavelet gave the shortest signals. It was able to summarize strain signals up to 77% and maintain more than 90% of the statistical parameters and the fatigue damage. Meanwhile the continuous and discrete Daubechies wavelet transforms summarized the signals below 60%. It proved that the Morlet wavelet was the best technique for fatigue data editing, especially for the automotive applications.