This study aims to assess the fatigue life analysis of strain signals with the implementation of a reconstruction of the signals of the discrete wavelet transform (DWT) decomposition technique. The process involves the time and frequency information used in fatigue life analysis. Strain signals, also known as original signals, were measured based on different road conditions. The DWT method was applied to the original signals to assess the time–frequency domain characteristics. As a result, 14 decomposition signals were obtained and the fatigue life of each signal was evaluated. The decomposition signals contained lower fatigue life, which is below 10⁸ cycles determined at levels 6 to 9. The reconstructed signals were obtained based on these selected levels. The frequency information obtained from the power spectral density (PSD) shows the elimination of high frequency, with low PSD amplitude in the reconstructed signal. The frequency range with high PSD amplitude obtained from this reconstructed signal at 20 Hz was the same as the original signal. Good fatigue life correlation between the reconstructed and original signals enable the prediction of similar fatigue life values, which are at 10⁷ and 10⁵ cycles for the highway and bumpy road conditions, respectively. It can be concluded that the implementation of this technique was able to provide good agreement of strain signals in fatigue life analysis.

这项研究的目的是通过重构离散小波变换（DWT）分解信号的方法来评估应变信号的疲劳寿命分析。该过程涉及疲劳寿命分析中使用的时间和频率信息。应变信号（也称为原始信号）是根据不同的路况进行测量的。DWT方法应用于原始信号，以评估时频域特征。结果，获得了14个分解信号，并评估了每个信号的疲劳寿命。分解信号包含较低的疲劳寿命，低于10 ⁸周期确定在6到9级。基于这些选定的级别获得了重建的信号。从功率谱密度（PSD）获得的频率信息表明，在重构信号中PSD幅度较低时，高频已消除。从该重构信号在20 Hz处获得的具有较高PSD幅度的频率范围与原始信号相同。重建信号和原始信号之间的良好疲劳寿命相关性可以预测相似的疲劳寿命值，在高速公路和崎bump不平的道路条件下，疲劳寿命值分别为10 ⁷和10 ⁵个循环。可以得出结论，该技术的实施能够在疲劳寿命分析中提供应变信号的良好一致性。