This paper presents the analysis of the influence of the shape of the probability distribution of the load course on the computational fatigue life. The literature on the subject shows that the main parameter describing the deviation from the normal (Gaussian) distribution is kurtosis and skewness. The authors proved that kurtosis parameter alone is not sufficient for this description of non-Gaussian signals in material fatigue. Kurtosis as well as the crest factor are both indicators which are sensitive to the shape of the loading signal. Due to the fact that the scatter of the model numerical simulations results is smaller in the case of kurtosis, it seems to be a better indicator for fatigue calculations. In addition, it was noted that an increase in kurtosis leads to a significant increase in the maximum values of the random stress history, which may lead to the formation of different fatigue phenomena in the components under tests, i.e. low-cycle fatigue phenomena may appear.

本文分析了载荷过程的概率分布形状对计算疲劳寿命的影响。关于该主题的文献表明，描述偏离正态（高斯）分布的主要参数是峰度和偏度。作者证明，峰度参数不足以描述材料疲劳中的非高斯信号。峰度以及波峰因数都是对加载信号的形状敏感的指标。由于在峰度情况下模型数值模拟结果的散布较小，因此它似乎是疲劳计算的更好指标。此外，应注意，峰度的增加会导致随机应力历史记录的最大值显着增加，