Purpose

The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve.

Design/methodology/approach

Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution.

Findings

By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method.

Originality/value

The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

中文翻译：

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引入误差椭圆的改进自举法用于疲劳寿命参数数值分析

目的

本文的目的是将误差椭圆引入自举法中，以提高小样本的可靠性和SN曲线的可信度。

设计/方法/方法

基于自举法和原始样本的可靠性，提出了两种误差椭圆模型。误差椭圆模型合理地预测扩展虚拟样本的离散定律服从二维正态分布。

发现

通过比较自举法，改进的自举法（正态分布）和误差椭圆法获得的参数，发现误差椭圆法实现了采样范围的扩大，缩短了置信区间，提高了参数估计的准确性。小样本。通过实例分析，证明了切线误差椭圆法是可行的，而切线误差椭圆法的SN曲线序列是合理的。

创意/价值

误差椭圆法可以为产品寿命的预测奠定技术基础，对产品质量评估具有渐进的意义。