Bayesian consideration of unknown sensor characteristics in fatigue-related structural health monitoring

Berg, Thomas; Ende, Sven von; Lammering, Rolf

doi:10.1016/j.probengmech.2019.02.001

Cited by 2 publications

(2 citation statements)

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“…The Bayesian solution originates from Jeffreys 3,4 and builds on the principles of Occam's razor is arguably the most popular and widely adopted is different domains particularly in fatigue. [5][6][7][8][9][10] Bayesian inference is aimed at computing a posterior probability distribution over a set of hypotheses or models, in terms of their relative support from the data. In this discussion I shall present some of them.…”

Section: Introductionmentioning

confidence: 99%

“…Many different approaches have been proposed in the statistical literature to help select the “best” model among a group of competing hypotheses. The Bayesian solution originates from Jeffreys 3,4 and builds on the principles of Occam's razor is arguably the most popular and widely adopted is different domains particularly in fatigue 5–10 . Bayesian inference is aimed at computing a posterior probability distribution over a set of hypotheses or models, in terms of their relative support from the data.…”

Section: Introductionmentioning

confidence: 99%

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Simulation‐based inference for reliability model selection and parameter calibration: An application to fatigue

Ben Abdessalem

2024

Quality & Reliability Eng

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Selecting a “useful” model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood‐free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis.

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