Research on the <i>P‐S‐N</i> Curve Fitting Method of Notched Specimens Considering Small Sample Properties

ABSTRACT Aiming at the issue of fatigue test data for large‐scale mechanical components of building steel are very limited, a method for fitting P‐S‐N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P‐S‐N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P‐S‐N curve is more reliable and accurate.

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Publication Details

Journal
Fatigue & Fracture of Engineering Materials & Structures
Published
2024-10-31
DOI
https://doi.org/10.1111/ffe.14490
Citations
3
Primary Topic
Fatigue and fracture mechanics
Type
article
Field-Weighted Citation Impact
0.81

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article

Research on the P‐S‐N Curve Fitting Method of Notched Specimens Considering Small Sample Properties

Jianhui Liu, Qingjun Wu, Ziyang Zhang, Shenglei Wu et al.
3 citations
Fatigue & Fracture of Engineering Materials & Structures
Fatigue and fracture mechanics
0.81
article

Research on the P‐S‐N Curve Fitting Method of Notched Specimens Considering Small Sample Properties

Jianhui Liu, Qingjun Wu, Ziyang Zhang, Shenglei Wu, Juntai Hu
article en
3 citations

Abstract

ABSTRACT Aiming at the issue of fatigue test data for large‐scale mechanical components of building steel are very limited, a method for fitting P‐S‐N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P‐S‐N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P‐S‐N curve is more reliable and accurate.

Fatigue & Fracture of Engineering Materials & StructuresVol. 48(1)
Lanzhou University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 32%
Fatigue and fracture mechanics
0.81
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