Share:


Evolution of corrosion of civil aircraft based on improved grey models

    Yiqiang Wang Affiliation
    ; Baohui Jia Affiliation

Abstract

Structure corrosion is one of the most common damages affecting the structural integrity of the aging civil aircraft. Three grey models were applied for predicting the corrosion evolution during aircraft maintenance checks. The developed models include the basic GM (1, 1) model and two improved models with the initial condition optimized by linear transformation and partial differential methods, respectively. Both improved models show better quantitative agreement with the existing data, while the model using the partial differential method exhibits the highest prediction accuracy amongst the three models presented above. Such models can also be used on the structure of other complex equipment to improve the efficiency of preventive maintenance.

Keyword : corrosion, civil aircraft, grey models, evolution, prediction

How to Cite
Wang, Y., & Jia, B. (2018). Evolution of corrosion of civil aircraft based on improved grey models. Aviation, 22(2), 55-59. https://doi.org/10.3846/aviation.2018.6011
Published in Issue
Oct 16, 2018
Abstract Views
810
PDF Downloads
507
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Altay, A., Ozkan, O., & Kayakutlu, G. (2014). Prediction of aircraft failure times using artificial neural networks and genetic algorithms. Journal of Aircraft, 51(1), 47-53. https://doi.org/10.2514/1.C031793

Dang, Y., Liu, S., & Liu, B. (2005). The GM models that x(n) be taken as initial value. Chinese Journal of Management Science, 13(1), 132-134.

Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294. https://doi.org/10.1016/S0167-6911(82)80025-X

He, Y., Li, C., & Zhang, T. (2016). Service fatigue life and service calendar life limits of aircraft structure: aircraft structural life envelope. Aeronautical Journal, 120(1233), 1746-1762. https://doi.org/10.1017/aer.2016.93

Hoeppner, D. W., & Arriscorreta, C. A. (2012). Exfoliation corrosion and pitting corrosion and their role in fatigue predictive modeling: State-of-the-art review. International Journal of Aerospace Engineering, 2012, Article ID 191879, 1-29. https://doi.org/10.1155/2012/191879

Jones, R. (2014). Fatigue crack growth and damage tolerance. Fatiuge and Fracture of Engineering Materials and Structures, 37(5), 463-483. https://doi.org/10.1111/ffe.12155

Liu, S., Tao, L., & Xie, N. (2016). On the new model system and framework of grey system theory. Journal of Grey System, 28(1), 1-15.

Liu, S., Zeng, B., Liu, J., Xie, N., & Yang, Y. (2015). Four basic models of GM(1, 1) and their suitable sequences. Grey Systems: Theory and Application, 5(2), 141-156. https://doi.org/10.1108/GS-04-2015-0016

Rathnayaka, R., Seneviratna, D., & Wei, J. (2015). Grey system based novel approach for stock market forecasting. Grey Systems: Theory and Application, 5(2), 178-193. https://doi.org/10.1108/GS-04-2015-0014

Shekhter, A., Crawford, B., & Loader, C. (2015). The effect of pitting corrosion on the safe-life prediction of the Royal Australian Air Force P-3C Orion aircraft. Engineering Failure Analysis, 55, 193-207. https://doi.org/10.1016/j.engfailanal.2015.05.020

Xu, S. (2015). Model for evaluating the commercial banks financial risk with interval grey uncertain linguistic variables. Journal of Intelligent and Fuzzy Systems, 28(2), 767-773. https://doi.org/10.3233/IFS-141358

Yang, L., Zhang, J., Guo, Y., & Wang, P. (2016). A Bayesian-based reliability estimation approach for corrosion fatigue crack growth utilizing the random walk. Quality and Reliability Engineering International, 32(7), 2519-2535. https://doi.org/10.1002/qre.1954

Zeng, B., Luo, C., & Liu, S. (2016). Development of an optimization method for the GM (1, N) model. Engineering Applications of Artificial Intelligence, 55, 353-362. https://doi.org/10.1016/j.engappai.2016.08.007