Statistical evaluation of fatigue tests using maximum likelihood

Störzel, Klaus; Baumgartner, J. Craig

doi:10.1515/mt-2020-0116

Cited by 22 publications

(19 citation statements)

References 18 publications

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“…11 summarizes the results of the fatigue tests at material and component level. The evaluation of the material test results in the form of Wöhler lines was carried out using the maximum likelihood method according to [7][8][9]. As expected, no component failure (crack initiation) was observed at load stages 1 and 2.…”

Section: Component Fatigue Test Resultsmentioning

confidence: 99%

Full-scale fatigue testing of a cast-iron wind turbine rotor shaft

Kyling

Siddiqui

2023

Forsch Ingenieurwes

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Spheroidal graphite cast iron (EN-GJS), which provides a high degree of design flexibility and the possibility for lightweight design, has benefits as a material for use in structural parts in wind turbines. Comparing components made using the sand casting technique to those made using the chill casting process reveals significant potential to boost strength. However, at present, there is neither a proven design guideline nor reliable material input data for a lightweight component based on this material and fabrication process. This publication presents the results from the Gusswelleproject in chronological order. It starts with the explanation of the final setup and test plan for the full-scale rotor shaft fatigue experiment. The elaborated sensor and operational concept are then presented together with an adequate finite element method (FEM) model of the specimen and relevant neighboring components. The validation of this FEM model to ensure that the loading and the resulting local strains representing the real test bench situation is described. The usage of non-destructive testing to document the condition of the specimen from initial crack formation until integrity loss is explained followed by a comparison between the component fatigue test results and the material-based life-time forecast. A strength increase for chill-cast large components in the range of 50% is indicated. Simulation-based crack propagation studies are performed to qualitatively verify the loads responsible for the observed cracks of the component test and to further develop the possible method for crack predictions.

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Section: Component Fatigue Test Resultsmentioning

confidence: 99%

Full-scale fatigue testing of a cast-iron wind turbine rotor shaft

Kyling

Siddiqui

2023

Forsch Ingenieurwes

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“…According to Störzel and Baumgartner, 49 the bad convergence of the maximum likelihood estimation can be avoided by computing a S‐N curve for multiple fixed values of

N_{normalk}

and choosing the best for the series according to the smallest standard deviation. This process cannot be applied to the approach described in Section 4.5 in the current work as the parameters of the S‐N curve should be continuous values.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…Next to the individual test results, also S‐N curves were considered as data basis for ML‐algorithms. For this, bilinear S‐N curves were computed for each test series using the maximum likelihood method 49 with parameters

S_{normalk}, N_{normalk}, k

, and

s

.…”

Section: Data Basementioning

confidence: 99%

“…Furthermore, the analytical functions in Table 1 are augmented with optimization parameters

p_{i}

and form a transformation function for the specimen data (Equation (6)). The objective of the optimization is to minimize the standard deviation of the S‐N curve from the transformed specimen data, estimated with Maximum Likelihood 49

σ_{normalA, trans} = σ_{normalA} f_{normalH} f_{normalV 90} f_{normald} f_{normalR}

…”

Section: Application Of Approachesmentioning

confidence: 99%

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New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

Leininger

Reissner

Baumgartner

2022

Fatigue Fract Eng Mat Struct

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Up to now, no consistent fatigue assessment approach of powder metallurgy (PM) components is available. For some materials and for some parameters, such as the density, a relationship to the fatigue strength is known; however, for other materials, such relationships are unknown. Based on an extensive data set with 828 test series, the present work addresses this problem by conceiving and applying five machine learning (ML)-based approaches to increase the accuracy of the prediction of the fatigue life as well as to predict the scatter of unknown data as precisely as possible. With the elaborated procedure, on the one hand, a scatter range of T S ¼ 1 : 1:40 can be achieved on completely unknown data. On the other hand, by using a newly defined loss function, the standard deviation of unknown data can be predicted very accurately. The findings provide the basis for further research on cost and efficiency optimized design of PM components through better estimation of fatigue life.

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“…Among the probability distributions used to analyze fatigue problems [9,10], the Weibull distribution is one of the most common model in the logarithmical scale. Weibull progressed a new approach and used it to study fatigue actual data [11,12].…”

Section: Probability Distributionsmentioning

confidence: 99%

A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications

Fakoor,

Kaklar

2023

Preprint

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Risk evaluation for fatigue failure of the engineering components is an important aspect of the engineering design. This study presents a probabilistic model for calculating Weibull distribution parameters to reduce the effect of percentage discretization error of experimental fatigue life and R-S-N curves for three reliability levels. The results show some error reduction in the Kolmogorov-Smirnov test and R-square values. Also, the Basquin model is used for different reliability levels with the same error order. The coefficient of variation for fatigue life increases at higher stress levels and has a linear relation with stress level for a high-cycle fatigue regime.

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Statistical evaluation of fatigue tests using maximum likelihood

Cited by 22 publications

References 18 publications

Full-scale fatigue testing of a cast-iron wind turbine rotor shaft

Full-scale fatigue testing of a cast-iron wind turbine rotor shaft

New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications

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