Hierarchical Bayesian fatigue data analysis

Liu, Xiaowei; Lü, Dagang; Hoogenboom, P.C.J.

doi:10.1016/j.ijfatigue.2017.03.043

Cited by 38 publications

(18 citation statements)

References 33 publications

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“…The probabilistic models for each of the random variables are summarized in Table 1. Two variables that can be monitored by the SHM system, the equivalent stress range and the annual fatigue load cycle, are modeled as a hierarchical Bayesian model, 49,50 as shown in Figure 11. Herein, the mean values of equivalent stress ranges MμΔSe and annual load cycles Mμv are modeled as random variables to represent the statistic uncertainty in their probabilistic model.…”

Section: Illustrative Examplementioning

confidence: 99%

VoI-informed decision-making for SHM system arrangement

Zhang

Qin

Lü

et al. 2020

Structural Health Monitoring

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Structural health monitoring systems have been widely implemented to provide real-time continuous data support and to ensure structural safety in the context of structural integrity management. However, the quantification of the potential benefits of structural health monitoring systems has not yet attracted widespread attention. At the same time, there is an urgent need to develop strategies, such as optimizing the monitoring period, monitoring variables, and other factors, to maximize the potential benefits of structural health monitoring systems. Considering the continuity of structural health monitoring information, a framework is developed in this article to support decision-making for structural health monitoring systems arrangement in the context of structural integrity management, which integrates the concepts of value of information and risk-based inspection planning based on an approach which utilizes a conjugate prior probability distribution for updating of the probabilistic models of structural performances based on structural health monitoring information. An example considering fatigue degradation of steel structures is investigated to illustrate the application of the proposed framework. The considered example shows that the choice of monitoring variables, the monitoring period, and the monitoring quality may be consistently optimized by the application of the proposed framework and approach. Finally, discussions and conclusions are provided to clarify the potential benefits of the proposed framework with a special view to practical applications of structural health monitoring systems.

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Section: Illustrative Examplementioning

confidence: 99%

VoI-informed decision-making for SHM system arrangement

Zhang

Qin

Lü

et al. 2020

Structural Health Monitoring

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“…Bayesian inference was employed in this paper to obtain stochastic estimates of the model parameters. The use of Bayesian inference in fatigue data analysis and design of experiments has recently been studied in [38,39].…”

Section: Parametersmentioning

confidence: 99%

Bayesian analysis of critical fatigue failure sources

Vaara

Väntänen²,

Kämäräinen

et al. 2020

International Journal of Fatigue

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…It should also be noted that other GoF tests are available, such as the Kolmogorov-Smirnov (KS) [9], Cramervon-Mises (CvM) [9], Akaike Information Criterion (AIC) [28] and Bayesian Information Criterion (BIC) [29].…”

Section: Class Validated Distribution Parameter Estimates? A-d Gof For Pplrmentioning

confidence: 99%

Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

Hoole

Sartor

Booker

et al. 2019

International Journal of Fatigue

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The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.

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Hierarchical Bayesian fatigue data analysis

Cited by 38 publications

References 33 publications

VoI-informed decision-making for SHM system arrangement

VoI-informed decision-making for SHM system arrangement

Bayesian analysis of critical fatigue failure sources

Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

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