Bayesian Optimised Collection Strategies for Fatigue Testing: Constant Life Testing: arXiv:2107.02685 [cond-mat.mtrl-sci]

Christopher Magazzeni; Rory Rose; Chris Gearhart; Jicheng Gong; Angus Wilkinson

doi:10.48550/arXiv.2107.02685

Bayesian Optimised Collection Strategies for Fatigue Testing: Constant Life Testing: arXiv:2107.02685 [cond-mat.mtrl-sci]

Christopher Magazzeni, Rory Rose, Chris Gearhart, Jicheng Gong, Angus Wilkinson

Center for Integrated Mobility Sciences

University of Oxford

Research output: Contribution to journal › Article

Abstract

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.

Original language	American English
Number of pages	32
Journal	ArXiv.org
DOIs	https://doi.org/10.48550/arXiv.2107.02685
State	Published - 2021

NREL Publication Number

NREL/JA-5400-80124

Keywords

Bayesian inference
fatigue testing

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10.48550/arXiv.2107.02685

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title = "Bayesian Optimised Collection Strategies for Fatigue Testing: Constant Life Testing: arXiv:2107.02685 [cond-mat.mtrl-sci]",

abstract = "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.",

keywords = "Bayesian inference, fatigue testing",

author = "Christopher Magazzeni and Rory Rose and Chris Gearhart and Jicheng Gong and Angus Wilkinson",

year = "2021",

doi = "10.48550/arXiv.2107.02685",

language = "American English",

journal = "ArXiv.org",

issn = "2331-8422",

publisher = "Cornell University",

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T2 - arXiv:2107.02685 [cond-mat.mtrl-sci]

AU - Magazzeni, Christopher

AU - Rose, Rory

AU - Gearhart, Chris

AU - Gong, Jicheng

AU - Wilkinson, Angus

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - fatigue testing

U2 - 10.48550/arXiv.2107.02685

DO - 10.48550/arXiv.2107.02685

M3 - Article

SN - 2331-8422

JO - ArXiv.org

JF - ArXiv.org

ER -

Bayesian Optimised Collection Strategies for Fatigue Testing: Constant Life Testing: arXiv:2107.02685 [cond-mat.mtrl-sci]

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Keywords

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