Reducing Uncertainty in Damage Growth Properties by Structural Health Monitoring
Structural health monitoring provides sensor data that monitor fatigue-induced damage growth in service. This information may in turn be used to improve the characterization of the material properties that govern damage propagation for the structure being monitored. These properties are often widely distributed between nominally identical structures because of differences in manufacturing processes and aging effects. The improved accuracy in damage growth characteristics allows more accurate prediction of the remaining useful life (RUL) of the structural component. In this paper, a probabilistic approach using Bayesian statistics is employed to progressively reduce the uncertainty in structure-specific damage growth parameters in spite of noise and bias in sensor measurements. Starting from an initial, wide distribution of damage parameters that are obtained from coupon tests, the distribution is progressively narrowed using the damage growth between consecutive measurements. Detailed discussions on how to construct the likelihood function under given noise of sensor data and how to update the distribution are presented. The approach is applied to crack growth in fuselage panels due to cycles of pressurization and depressurization. It is shown that the proposed method rapidly converges to the accurate damage parameters when the initial damage size is 20mm and there is no bias in the measurements. Fairly accurate material properties can be obtained also with measurement errors of 5mm. Using the identified damage parameters, it is shown that the 95% conservative RUL converges to the true RUL from the conservative side. The proposed approach may have the potential of turning aircraft into flying fatigue laboratories.
How to Cite
crack detection, damage detection, damage propagation model, fatigue crack growth, structural health management, structural health monitoring,, applications: aviation
P. Berruet, A. K. A. Toguyeni, and E. Craye. (1999, Structural and functional approach for dependability in FMS, in IEEE International Conference on Systems, Man, and Cybernetics
S. J. Engel, B. J. Gilmartin, K. Bongort, and A. Hess. (2000, March 18-25), Prognostics, the real issues involved with predicting life remaining, in IEEE Aerospace Conference, Big Sky, MT.
K. Goebel, B. Saha, A. Saxena, N. Mct, and N. Riacs. (2008). A comparison of three data-driven techniques for prognostics,
J. Gu, D. Barker, and M. Pecht. (2007), "Uncertainty Assessment of Prognostics of Electronics Subject to Random Vibration," in AAAI Fall Symposium on Artificial Intelligence for Prognostics, pp. 50-57.
H. H. Harkness. (1994). Computational methods for fracture mechanics and probabilistic fatigue. Ph.D. thesis, the Northwestern University, Evanston, IL
L. C. Jaw, S. M. Inc, and A. Z. Tempe. (1999, Neural networks for model-based prognostics, in IEEE Aerospace Conference
A. A. Kale, R. T. Haftka, M. Papila, and B. V. Sankar. (2003), "Tradeoff for Weight and Inspection Cost for Fail-Safe Design," in 44th AIAA/ASME/SDM conference Norfolk, VA.
A. A. Kale, R. T. Haftka, and B. V. Sankar. (2004, Tradeoff of structural weight and inspection cost in reliability based optimization using multiple inspection types, in 10th AIAA/ ISSMO Multidisciplinary analysis and optimization conference, Albany, NY
A. A. Kale, R. T. Haftka, and B. V. Sankar. (2008). Efficient Reliability-Based Design and Inspection of Stiffened Panels Against Fatigue, Journal OF Aircraft, vol. 45, p. 86.
C. J. Li and H. Lee. (2005). Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics, Mechanical systems and signal processing, vol. 19, pp. 836-846.
K. Y. Lin, D. T. Rusk, and J. J. Du. (2002). Equivalent level of safety approach to damage-tolerant aircraft structural design, Journal of Aircraft, vol. 39, pp. 167-174.
J. Luo, M. Namburu, K. Pattipati, L. Qiao, M. Kawamoto, and S. Chigusa. (2003, Model-based prognostic techniques [maintenance applications], in IEEE Systems Readiness Technology Conference
G. C. Montanari. (1995). Aging and life models for insulation systems based on PD detection, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, pp. 667-675.
J. C. Newman, E. P. Phillips, and M. H. Swain. (1999). Fatigue-life prediction methodology using small- crack theory, International Journal of fatigue, vol. 21, pp. 109-119.
M.Niu. (1990), "Airframe Structural Design," in Fatigue, Damage Tolerance and Fail-Safe Design, Conmilit Press LTD., Ed. Hong Kong, pp. 538-570.
M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, and G. Vachtsevanos. (2008, Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics, in International Conference on Prognostics and Health Management,, Denver, CO
M. Orchard, B. Wu, and G. Vachtsevanos. (2005, A Particle Filter Framework for Failure Prognosis, in World Tribology Congress III, Washington, D.C.
P. C. Paris, H. Tada, and J. K. Donald. (1999). Service load fatigue damage—a historical perspective, International Journal of fatigue, vol. 21, pp. 35-46.
A. Ray and R. Patankar. (1999). A stochastic model of fatigue crack propagation under variable-amplitude loading, Engineering Fracture Mechanics, vol. 62, pp. 477-493.
A. Ray and S. Tangirala. (1996). Stochastic modeling of fatigue crack dynamics for on-line failure prognostics, IEEE Transactions on Control Systems Technology, vol. 4, pp. 443-451.
B. Saha and K. Goebel. (2008, Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques, in IEEE Aerospace Conference
M. A. Schwabacher. (2005, A survey of data-driven prognostics, in AIAA Infotech@Aerospace Conference, Reston, VA
J. W. Sheppard, M. A. Kaufman, A. Inc, and M. D. Annapolis. (2005). Bayesian diagnosis and prognosis using instrument uncertainty, IEEE Autotestcon, 2005, pp. 417-423.
A. N. Srivastava and S. Das. (2009). Detection and Prognostics on Low Dimensional Systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 39, pp. 44- 54.
M. Tipping. (2000), "The Relevance Vector Machine. in Advances in Neural Information Processing Systems," MIT Press, Cambridge.
L. Wang and F. G. Yuan. (2005). Damage identification in a composite plate using prestack reverse-time migration technique, Structural Health Monitoring, vol. 4, pp. 195-211.
Y. Xue, D. L. McDowell, M. F. Horstemeyer, M. H. Dale, and J. B. Jordon. (2007). Microstructure- based multistage fatigue modeling of aluminum alloy 7075-T651, Engineering Fracture Mechanics, vol. 74, pp. 2810-2823.
J. Yan and J. Lee. (2007, A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operations, in IEEE International Conference on Automation and Logistics
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