University of Oulu

Miikka Väntänen, Joona Vaara, Jukka Kemppainen, Tero Frondelius, Bayesian analysis of fatigue data with multi-load-level damage accumulation: The benefits of rerun specimens, International Journal of Fatigue, Volume 138, 2020, 105601, ISSN 0142-1123, https://doi.org/10.1016/j.ijfatigue.2020.105601

Bayesian analysis of fatigue data with multi-load-level damage accumulation : the benefits of rerun specimens

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Author: Väntänen, Miikka1; Vaara, Joona2; Kemppainen, Jukka3;
Organizations: 1Global Boiler Works Oy, Lumijoentie 8, 90400 Oulu, Finland
2Wärtsilä, Järvikatu 2-4, 65100 Vaasa, Finland
3University of Oulu, Pentti Kaiteran katu 1, 90014 Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2020111290015
Language: English
Published: Elsevier, 2020
Publish Date: 2022-04-07
Description:

Abstract

A hierarchical Bayesian approach to analysing fatigue test data including reinserted specimens is proposed. It is found that the inference model is capable of fitting the fatigue damage model to the observed data well. After the addition of rerun specimen data, the results show a significant change in the predictive SN curves. For the analysed 40CrMo8 fatigue data sets, the deviation of fatigue limit is the primary explanatory mechanism for the observed fatigue life scatter. The change of predictive fatigue limit distribution after the addition of rerun data is compared to the change due to additional (simulated) virgin specimen tests.

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Series: International journal of fatigue
ISSN: 0142-1123
ISSN-E: 1879-3452
ISSN-L: 0142-1123
Volume: 138
Article number: 105601
DOI: 10.1016/j.ijfatigue.2020.105601
OADOI: https://oadoi.org/10.1016/j.ijfatigue.2020.105601
Type of Publication: A1 Journal article – refereed
Field of Science: 111 Mathematics
112 Statistics and probability
214 Mechanical engineering
Subjects:
Funding: The authors would like to acknowledge the financial support of Business Finland for WIMMA Wärtsilä Dnro 1566/31/2015, ISA Wärtsilä Dnro 7734/31/2018 and ISA GBW Dnro 7752/31/18.
Copyright information: © 2020 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/