Solid Mechanics and Vehicle Conceptual Design

Reliability modelling for fatigue based on uncertain differential equation

  • LI Xiaoyang ,
  • TAO Zhao ,
  • ZHANG Wei
Expand
  • 1. School of Reliability and Systems Engineering, Beihang University, Beijing 100083, China;
    2. Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing 100083, China

Received date: 2021-05-17

  Revised date: 2021-08-18

  Online published: 2021-08-17

Supported by

National Natural Science Foundation of China (51775020,51875019);Science Challenge Project (TZ2018007);Fundamental Research Funds for the Central Universities (YWF-21-BJ-J-515)

Abstract

To quantify the dynamic characteristic of uncertainty in the fatigue crack growth process in the time dimension, the uncertain differential equation of uncertainty theory is introduced to describe the dynamic characteristic of uncertainty from a small time scale (i.e., smaller time unit macroscopically). Specifically, for the crack growth process considering the crack closure and the retardation effect caused by overloads, the dynamic characteristic of the uncertainty in the time dimension and the static characteristic of the uncertainties in the physical properties, the external load and the crack threshold are considered and quantified in the framework of uncertainty theory. A fatigue crack growth model based on the uncertain differential equation is built from the small time scale. The margin equation is constructed regarding the crack length as the performance parameter, and the reliability function is deduced for the fatigue reliability modelling. The proposed model is applied to a case of fatigue crack growth experiment, and the reliability evaluation and the prediction of crack growth are obtained. The discussion and analysis of the proposed model shows that the modelling of fatigue crack growth from the small time scale and the careful classification and scientific quantification of the uncertainties are significant.

Cite this article

LI Xiaoyang , TAO Zhao , ZHANG Wei . Reliability modelling for fatigue based on uncertain differential equation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(8) : 225820 -225820 . DOI: 10.7527/S1000-6893.2021.25820

References

[1] VIRKLER D A, HILLBERRY B M, GOEL P K. The statistical nature of fatigue crack propagation[J]. Journal of Engineering Materials and Technology, 1979, 101(2): 148-153.
[2] 邢修三. 疲劳断裂非平衡统计理论: Ⅰ.疲劳微裂纹长大的位错机理和统计特性[J]. 中国科学 (A辑), 1986, 16(5): 501-510. XING X S. Nonequilibrium statistical theory of fatigue fracture——Ⅰ. Dislocation mechanism and statistical properties of fatigue microcrack growth[J]. Science in China, SerA, 1986, 16(5): 501-510 (in Chinese).
[3] KRAUSZ A S. Fracture Kinetics of crack growth [M]. Dordrecht:Kluwer Academic Publishers, 1988.
[4] BOGDANOFF J L, KOZIN F. Probabilistic models of cumulative damage[M]. New York: Wiley-Interscience, 1985.
[5] BEN ABDESSALEM A, AZAÏS R, TOUZET-CORTINA M, et al. Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2016, 230(4): 405-416.
[6] HAO S H, YANG J, BERENGUER C. Nonlinear step-stress accelerated degradation modelling considering three sources of variability[J]. Reliability Engineering & System Safety, 2018, 172: 207-215.
[7] SI X S, WANG W B, HU C H, et al. Remaining useful life estimation-A review on the statistical data driven approaches[J]. European Journal of Operational Research, 2011, 213(1): 1-14.
[8] LIN Y K, YANG J N. On statistical moments of fatigue crack propagation[J]. Engineering Fracture Mechanics, 1983, 18(2): 243-256.
[9] LIN Y K, YANG J N. A stochastic theory of fatigue crack propagation[J]. AIAA Journal, 1985, 23(1): 117-124.
[10] LI Y Z, ZHU S P, LIAO D, et al. Probabilistic modeling of fatigue crack growth and experimental verification[J]. Engineering Failure Analysis, 2020, 118: 104862.
[11] SOBCZYK K. Modelling of random fatigue crack growth[J]. Engineering Fracture Mechanics, 1986, 24(4): 609-623.
[12] SPENCER B F, TANG J, ARTLEY M E. Stochastic approach to modeling fatigue crack growth[J]. AIAA Journal, 1989, 27(11): 1628-1635.
[13] SHARIFF A A. A stochastic paris-erdogan model for fatigue crack growth using two-state model[J]. Bulletin of the Malaysian Mathematical Society, 2008, 1(1): 97-108.
[14] EROGLU E, GUNEY I, GUNES I. Fatigue test with stochastic differential equation modeling[J]. Acta Physica Polonica A, 2012, 121(1): 36-38.
[15] ALLEN E J. SDE models with exponential drift and diffusion for approximating fatigue crack growth dynamics[J]. Engineering Fracture Mechanics, 2018, 200: 75-85.
[16] GARDINER C W. Handbook of stochastic methods for physics, chemistry and the natural sciences[M]. Berlin: Springer Berlin Heidelberg, 1985.
[17] LIU B D. Uncertainty theory[M]. Berlin: Springer Berlin Heidelberg, 2015.
[18] LIU B D. Fuzzy process, hybrid process and uncertain process[J]. Journal of Uncertain Systems, 2008, 2(1): 3-16.
[19] LI X Y, TAO Z, WU J P, et al. Uncertainty theory based reliability modeling for fatigue[J]. Engineering Failure Analysis, 2021, 119: 104931.
[20] WOLF E. Fatigue crack closure under cyclic tension[J]. Engineering Fracture Mechanics, 1970, 2(1): 37-45.
[21] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 金属材料 疲劳试验 疲劳裂纹扩展方法: GB/T 6398—2017[S]. 北京: 中国标准出版社, 2017. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. Metallic materials—Fatigue testing—Fatigue crack growth method: GB/T 6398—2017[S]. Beijing: Standards Press of China, 2017 (in Chinese).
[22] ZHANG W, LIU Y M. SEM testing for crack closure investigation and virtual crack annealing model development[J]. International Journal of Fatigue, 2012, 43: 188-196.
[23] HU D Y, SU X, LIU X, et al. Bayesian-based probabilistic fatigue crack growth evaluation combined with machine-learning-assisted GPR[J]. Engineering Fracture Mechanics, 2020, 229: 106933.
[24] 康锐. 确信可靠性理论与方法[M]. 北京: 国防工业出版社, 2020. KANG R. Belief reliability theory and methodology[M]. Beijing: National Defense Industry Press, 2020 (in Chinese).
[25] LIU Z. Generalized moment estimation for uncertain differential equations[J]. Applied Mathematics and Computation, 2021, 392: 125724.
[26] LI X Y, CHEN W B, LI F R, et al. Reliability evaluation with limited and censored time-to-failure data based on uncertainty distributions[J]. Applied Mathematical Modelling, 2021, 94: 403-420.
Outlines

/