Research on fatigue life evaluation method of shafts based on small sample P–S–N

Liu, Jianhui; Hua, Feilong; Zhao, He; Pan, Xuemei; Xie, Linghai

doi:10.1016/j.ijpvp.2022.104773

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…The random variable N f is independent in the principle of probability theory, and its approximate limit state function is as follows (Liu et al, 2016(Liu et al, , 2022bKoyluoglu and Nielsen, 1994):…”

Section: Parameter Determination Methods Of Weibull Distributionmentioning

confidence: 99%

“…The random variable Nf is independent in the principle of probability theory, and its approximate limit state function is as follows (Liu et al ., 2016, 2022b; Koyluoglu and Nielsen, 1994):where the mean value μX={μx1,μx2,⋯,μxn}T, the standard deviation σX=(σX1,σX2,...,σXn)T, ∇g(μX) represents the gradient of g at point μX, ∇g(μX)={∂g(μX)∂x1,∂g(μX)∂x2,⋯,∂g(μX)∂xn...…”

Section: Fatigue Life Prediction Modelmentioning

confidence: 99%

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Research on fatigue life evaluation method of shaft parts based on small sample

Liu

Zhang

Zhu

et al. 2023

IJSI

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PurposeDue to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.Design/methodology/approachFirst, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.FindingsThe TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.Originality/valueThe purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

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Section: Parameter Determination Methods Of Weibull Distributionmentioning

confidence: 99%

Section: Fatigue Life Prediction Modelmentioning

confidence: 99%

Research on fatigue life evaluation method of shaft parts based on small sample

Liu

Zhang

Zhu

et al. 2023

IJSI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bai et al 18 and Li et al 19 improved this method respectively. And Liu and Pan et al 20,21 applied the bootstrap method to expand the fatigue data under the highest load level into large samples and applied the backward statistical inference method to curve fitting.…”

Section: Introductionmentioning

confidence: 99%

P–S–N curves fitting method of small samples obeying Weibull distribution

Song,

Zhao,

Xie

et al. 2024

Fatigue Fract Eng Mat Struct

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A three‐parameter Weibull small sample aggregation method is proposed to address the problem of small sample fatigue data in practical engineering. First, based on the empirical assumption that the shape parameter is a constant and data equivalent conversion and aggregation, the small sample fatigue life data are converted into “equivalent large samples.” Second, through the linear relationship between logarithmic scale parameter and stress, an iterative procedure is established to estimate the scale parameters of small sample data. At the same time, the moment and rank estimation methods are used to solve the location parameters quickly. Finally, P–S–N curves with different survival rates are given, and a 7‐series aluminum alloy is selected to verify the correctness and reliability of the method proposed in this study. The results show that the predicted life obtained by the method still has high accuracy under a high survival rate, which can provide a theoretical basis for solving practical engineering problems.

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“…Converting multiaxial loads to uniaxial stresses via the stress equivalence criterion and using probabilistic S-N curves are another common methods to study the uncertainty of multiaxial fatigue life. [22][23][24] Gao et al 25 analyzed the probabilistic fatigue life of fiber composite materials based on a probabilistic S-N curve, combining Markov Monte Carlo algorithm and Bayesian inference. The S-N curve often demands a substantial amount of material tests, which is time-consuming and costly, thus limiting its application in engineering to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

“…While this approach is simple, both its accuracy and efficiency can significantly wane for nonlinear and implicit functions that are prevalent in engineering applications. Converting multiaxial loads to uniaxial stresses via the stress equivalence criterion and using probabilistic S‐N curves are another common methods to study the uncertainty of multiaxial fatigue life 22–24 . Gao et al 25 analyzed the probabilistic fatigue life of fiber composite materials based on a probabilistic S‐N curve, combining Markov Monte Carlo algorithm and Bayesian inference.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic life prediction framework for vibration fatigue of turbine blade based on refined polynomial chaos expansion

Fang,

Liu,

et al. 2024

Fatigue Fract Eng Mat Struct

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Vibration fatigue is a typical form of multiaxial fatigue. In this study, initial vibration fatigue tests were conducted on rocket turbine blades, and significant dispersion in fatigue life was found. Consistent with this finding, a novel probabilistic life prediction framework was proposed. This framework integrates sensitivity analysis and sequential sampling technology and introduces polynomial chaos expansion as a computationally efficient alternative to finite element analysis. And a continuous mechanics‐based damage evolution model was employed to examine the vibration fatigue life of turbine blades. The findings validate the effectiveness of the framework, as no significant difference was found between the experimental results and simulated predictions at the 95% confidence level. Furthermore, comparison with the Monte Carlo simulation indicated that this framework achieves comparable prediction accuracy, while significantly reducing the required samples by 2 orders of magnitude, which effectively addresses the fatigue problem of small sample data. This framework enables rapid and accurate multiaxial fatigue probabilistic life prediction, which holds important implications for the reliability design of reusable spacecraft.

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Research on fatigue life evaluation method of shafts based on small sample P–S–N

Cited by 8 publications

References 27 publications

Research on fatigue life evaluation method of shaft parts based on small sample

Research on fatigue life evaluation method of shaft parts based on small sample

P–S–N curves fitting method of small samples obeying Weibull distribution

A probabilistic life prediction framework for vibration fatigue of turbine blade based on refined polynomial chaos expansion

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