Accelerated Fatigue Tests for reliability estimation of chassis parts

P. Beaumont, F. Guérin, P. Lantiéri, M. Facchinetti, G. M. Borret
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引用次数: 1

Abstract

In order to assess the fatigue strength, the StairCase method is extensively applied, thanks to its independence from physical parameters and because of it provides reliable results using few parts, thus involving low testing time. Nevertheless, the Dixon &Mood (D&M) estimation of StairCase results does not always permit to obtain a reliable estimation of the scatter parameter for the fatigue strength (section 3.1), which is an essential feature for the overall failure risk management. Moreover, we even search for a more reliable mean estimation. The likelihood estimation, used in the Maximum Likelihood Estimation of the fatigue limit (MLE) (.3.2) and in Accelerated Life Testing estimation of the number of cycles to failure (ALT) (3.3), includes hypothesis on the mechanical acceleration model but leads to obtain good estimations of each parameter included in the method. We saw that for large sample we found a good estimation with all the methods: both D&M and MLE approaches give good estimations of the fatigue strength distribution, and the ALT approach gives a good estimation of the number of cycles to failure distribution. For small samples, i.e. the most common situation in the industry, we have found, on average, a good estimation of the mean, both on the fatigue strength's mean and on the number of cycles to failure's mean. But best estimations are found by MLE or ALT method. For the standard deviation estimation it is clear that the D&M estimation cannot be applied to small samples. For MLE estimation we have many outliers which may be removed by analyzing special cases giving those values.
底盘零件可靠性评估的加速疲劳试验
为了评估疲劳强度,由于其不依赖于物理参数,并且由于它使用较少的部件提供可靠的结果,因此涉及的测试时间较短,因此楼梯法被广泛应用。尽管如此,Dixon &Mood (D&M)对StairCase结果的估计并不总是允许获得疲劳强度散射参数的可靠估计(第3.1节),而疲劳强度散射参数是整体失效风险管理的基本特征。此外,我们甚至寻找一个更可靠的平均估计。在疲劳极限的最大似然估计(MLE)(.3.2)和加速寿命试验的失效循环数估计(ALT)(3.3)中使用的似然估计包括对机械加速度模型的假设,但导致对方法中包含的每个参数得到很好的估计。我们看到,对于大样本,我们发现所有方法都有很好的估计:D&M和MLE方法都能很好地估计疲劳强度分布,ALT方法能很好地估计失效分布的循环次数。对于小样本,即行业中最常见的情况,我们发现,平均而言,对疲劳强度平均值和失效平均值的循环次数都有很好的估计。但最好的估计是用MLE或ALT方法。对于标准差估计,D&M估计显然不能应用于小样本。对于MLE估计,我们有许多异常值,可以通过分析给出这些值的特殊情况来去除。
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