Bayesian optimized collection strategies for fatigue strength testing
Bayesian optimized collection strategies for fatigue strength testing
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DOI：
10.1111/ffe.13859
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
C. M. Magazzeni;Rory Rose;Chris Gearhart;J. Gong;A. Wilkinson
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文献类型：
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作者：
C. M. Magazzeni;Rory Rose;Chris Gearhart;J. Gong;A. Wilkinson
一个统计框架，使最佳的采样和分析等寿命疲劳数据。基于传统的阶梯法和应力步长法建立了贝叶斯最大熵抽样协议，降低了数据采集对先验知识的要求。贝叶斯阶梯方法显示出改进的参数估计效率，贝叶斯应力步长方法在较大步长下显示出与标准方法相同的精度，从而允许实验人员减少对加载历史的关注。显示了用于确定模型适用性的统计方法，突出了方案的影响。进行实验验证，显示在实验室测试的方法的适用性。
A statistical framework is presented enabling optimal sampling and analysis of constant life fatigue data. Protocols using Bayesian maximum entropy sampling are built based on conventional staircase and stress step methods, reducing the requirement of prior knowledge for data collection. The Bayesian Staircase method shows improved parameter estimation efficiency, and the Bayesian Stress Step method shows equal accuracy to the standard method at larger step size allowing experimentalists to lessen concerns of loading history. Statistical methods for determining model suitability are shown, highlighting the influence of protocol. Experimental validation is performed, showing the applicability of the methods in laboratory testing.