基于数据融合的疲劳寿命预测方法
Fatigue life prediction method based on data fusion
查看参考文献28篇
|
文摘
|
针对疲劳实验耗时长、实验数据分散性大,通过小样本数据获得的高存活率P-S-N曲线不够准确,疲劳寿命预测不够准确和可靠的问题,基于性能-寿命概率映射原理数据融合方法对不同应力级的小样本疲劳数据进行数据融合,并分析和评估通过该方法获得准确P-S-N曲线的可行性。与融合前的小样本疲劳数据相比,数据融合后所得P-S-N曲线更接近总体大样本数据得出的P-S-N曲线,表明该方法能够在减少疲劳实验量的前提下有效提高疲劳寿命预测的可靠性与准确性。对比和评价不同模型对融合前与融合后数据的寿命预测能力,发现三参数幂函数模型的预测能力较强,而对于大样本数据,四种模型(Basquin S-N模型、指数S-N模型、三参数幂函数S-N模型(基于对数正态分布)、三参数幂函数S-N模型(基于三参数威布尔分布))的预测能力很接近。 |
|
其他语种文摘
|
To address the challenges posed by the time-consuming nature of fatigue test and the scattered nature of test data,it is evident that P-S-N curves derived from small samples with high survival rates lack sufficient accuracy, leading to unreliable predictions of fatigue life. The data fusion method based on the performance-life probability mapping principle is used to fuse small sample fatigue data of different stress levels, and the feasibility of obtaining accurate P-S-N curves by this method is analyzed and evaluated. The results demonstrated that P-S-N curves obtained post-fusion are closer to the P-S-N curve derived from larger sample datasets. This approach effectively enhances both reliability and accuracy in predicting fatigue life while simultaneously reducing the amount of required fatigue tests. A comparative evaluation is conducted on the predictive capabilities for fatigue life before and after fusion using different models; notably, it is found that the three-parameter power function model demonstrates superior predictive ability, whereas when ample fatigue data is available, the prediction capabilities among four models(Basquin S-N model, exponential S-N model, three-parameter power function S-N model(based on lognormal distribution), and three-parameter power function S-N model(based on three-parameter Weibull distribution) exhibit a considerable degree of resemblance. |
|
来源
|
航空材料学报
,2024,44(6):107-115 【核心库】
|
|
DOI
|
10.11868/j.issn.1005-5053.2023.000017
|
|
关键词
|
疲劳
;
数据融合
;
应力寿命模型
;
寿命预测
;
模型对比
|
|
地址
|
重庆大学航空航天学院, 重庆, 400044
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1005-5053 |
|
学科
|
一般工业技术;航空 |
|
基金
|
国家重大科技专项
;
国家重点研发计划项目
|
|
文献收藏号
|
CSCD:7851444
|
参考文献 共
28
共2页
|
|
1.
Kim W H. Crack nucleation and stageⅠpropagation in high strain fatigue-Ⅱmechanism.
Acta Metallurgica,1978,26(5):789-799
|
CSCD被引
5
次
|
|
|
|
|
2.
Murakami Y. What is fatigue damage? A view point from the observation of low cycle fatigue process.
International Journal of Fatigue,2005,27(8):991-1005
|
CSCD被引
11
次
|
|
|
|
|
3.
Gao G. Mechanism of subsurface microstructural fatigue crack initiation during high and very-high cycle fatigue of advanced bainitic steels.
Journal of Materials Science & Technology,2022,108:142-157
|
CSCD被引
1
次
|
|
|
|
|
4.
Zhang M. The fatigue behavior and mechanism of FV520B-Ⅰwith large surface roughness in a very high cycle regime.
Engineering Failure Analysis,2016,66:432-444
|
CSCD被引
7
次
|
|
|
|
|
5.
Heinz S. Analysis of fatigue properties and failure mechanisms of Ti6Al4V in the very high cycle fatigue regime using ultrasonic technology and 3D laser scanning vibrometry.
Ultrasonics,2013,53(8):1433-1440
|
CSCD被引
5
次
|
|
|
|
|
6.
Stephens R I.
Metal fatigue in engineering,2000
|
CSCD被引
11
次
|
|
|
|
|
7.
Hoole J. Systematic statistical characterisation of stress-life datasets using 3-parameter distributions.
International Journal of Fatigue,2019:105216
|
CSCD被引
2
次
|
|
|
|
|
8.
Bai X. New method for determining P-S-N curves in terms of equivalent fatigue lives.
Fatigue & Fracture of Engineering Materials & Structures,2019,42(10):2340-2353
|
CSCD被引
2
次
|
|
|
|
|
9.
白恩军. 威布尔分布下小样本P-S-N曲线拟合方法.
西安交通大学学报,2019,53(9):96-101
|
CSCD被引
6
次
|
|
|
|
|
10.
Liu X. Small sample based fatigue reliability analysis using non-intrusive polynomial chaos.
IEEE Access,2020,8:59678-59683
|
CSCD被引
1
次
|
|
|
|
|
11.
Li C. Determination of the fatigue P-S-N curves-a critical review and improved backward statistical inference method.
International Journal of Fatigue,2020,139:105789
|
CSCD被引
2
次
|
|
|
|
|
12.
Huang H Z. Fatigue lifetime assessment of aircraft engine disc via multi-source information fusion.
International Journal of Turbo & Jet-Engines,2014,31(2):167-174
|
CSCD被引
3
次
|
|
|
|
|
13.
Dabetwar S. Fatigue damage diagnostics of composites using data fusion and data augmentation with deep neural networks.
Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems,2022,5(2):021004
|
CSCD被引
1
次
|
|
|
|
|
14.
Chen J. Uncertainty quantification of fatigue S-N curves with sparse data using hierarchical Bayesian data augmentation.
International Journal of Fatigue,2020,134:105511
|
CSCD被引
3
次
|
|
|
|
|
15.
Basquin O H. The exponential law of endurance tests.
Proceedings of ASTM,1910,10:625-630
|
CSCD被引
3
次
|
|
|
|
|
16.
高镇同.
疲劳可靠性,2000
|
CSCD被引
105
次
|
|
|
|
|
17.
傅惠民. P-S-N曲线拟合法.
航空学报,1988,9(7):338-341
|
CSCD被引
9
次
|
|
|
|
|
18.
凌丹.
威布尔分布模型及其在机械可靠性中的应用研究,2011
|
CSCD被引
19
次
|
|
|
|
|
19.
张国栋. 定向合金DZ125热/机械疲劳寿命预测模型评估.
航空动力学报,2004,19(1):17-22
|
CSCD被引
9
次
|
|
|
|
|
20.
谢里阳. 样本信息聚集原理与P-S-N曲线拟合方法.
机械工程学报,2013,49(15):96-104
|
CSCD被引
14
次
|
|
|
|
|
了解气象卫星更多信息，请访问