Fatigue crack initiation and growth during the service of aging aircraft are important life- limiting phenomena. In a previous study, a risk prediction and reliability model for naval aircraft has been developed based on fracture mechanics and inspection field data. Despite significant achievements in the study of fatigue cracks using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth using non-destructive inspection and to integrate the inspection results with the fracture mechanics models to improve the predictions. In this paper, a probabilistic damage-tolerance model based on acoustic emission monitoring is proposed to enhance the reliability and risk prediction for structures subject to fatigue cracking. Experiments were carried out to estimate the stress intensity range ∆K, during fatigue crack propagation using acoustic emission (AE) inspection. The uncertainty of parameters is captured via probability distribution functions. Bayesian regression technique was used to estimate the marginal and joint probability distributions of model parameters. Finally, an AE-based risk factor is defined as the probability of transitioning from stage II to stage III of fatigue crack growth regime. This transition probability is calculated as the probability that the maximum stress intensity exceeds the fracture toughness of the material at a given point in time, based on the AE inspection results.
How to Cite
crack detection, damage detection, damage propagation model, data driven prognostics, fatigue crack growth, materials damage prognostics, structural health managemen, structural health monitoring, applications: aviation
(ASTM E647-08, 2008) ASTM E647-08. Standard Test Method for Measurement of Fatigue Crack Growth Rates. ASTM International, 2008.
(Azarkhail and Modarres, 2007) Azarkhail, M., and M. Modarres. A Novel Bayesian Framework for Uncertainty Management in Physics-Based Reliability Models. In ASME International Mechanical Engineering Congress and Exposition.
Seattle, Washington, USA, November 11, 2007. (Bassim et al., 1994) Bassim, M. N., S. St Lawrence, and C. D. Liu. Detection of the onset of fatigue crack growth in rail steels using acoustic emission. Engineering Fracture Mechanics 47, no. 2: 207-
(Berkovits and Fang, 1995) Berkovits, Avraham, and Daining Fang. Study of fatigue crack characteristics by acoustic emission. Engineering Fracture Mechanics 51, no. 3 (June): 401-409, 1995.
(Boller, 2001) Boller, C. Ways and options for aircraft structural health management. Smart materials and structures 10, no. 3: 432-440, 2001.
(Fang and Berkovits, 1993) Fang, D., and A. Berkovits. Fatigue damage mechanisms on the basis of acoustic emission measurements. In Novel experimental techniques in fracture mechanics: presented at the 1993 ASME Winter Annual Meeting New Orleans, Louisiana November 28- December 3, 1993, 219. American Society of Mechanical Engineers, 1993.
(Hamel et al., 1981) Hamel, F., J. P. Bailon, and M. N. Bassim. Acoustic emission mechanisms during high-cycle fatigue. Engineering Fracture Mechanics 14, no. 4: 853-860, 1981.
(Mix, 2005) Mix, P. E. Introduction to nondestructive testing: a training guide. Wiley-Interscience, 2005. (Modarres et al., 1999) Modarres, M., M. Kaminskiy,
and V. Krivtsov. Reliability engineering and risk analysis: a practical guide. CRC Press, 1999. (Roberts and Talebzadeh, 2003) Roberts, T. M., and M. Talebzadeh. Acoustic emission monitoring of fatigue crack propagation. Journal of Constructional Steel Research 59, no. 6 (June):695-712, 2003.
(Wang et al., 2008) Wang, X., M. Rabiei, M.
Modarres, and P. Hoffman. A probability-based individual aircraft tracking approach for airframe integrity. In Aging Aircraft 2008. Phoenix AZ, April, 2008.
(Wang et al., 2009) Wang, X., M. Rabiei, J. Hurtado, M. Modarres, and P. Hoffman. A probabilistic- based airframe integrity management model. Reliability Engineering & System Safety 94, no. 5 (May): 932-941, 2009.
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