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A cost driven predictive maintenance policy for structural airframe maintenance

Abstract : This paper proposes a model-based prognostics method that couples the Extended Kalman Filter (EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo (MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.
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Contributor : Nicolas Binaud <>
Submitted on : Monday, February 4, 2019 - 11:43:40 AM
Last modification on : Wednesday, June 24, 2020 - 4:18:55 PM


  • HAL Id : hal-02005843, version 1


Yiwei Wang, Christian Gogu, Nicolas Binaud, Christian Bes, Jian Fu. A cost driven predictive maintenance policy for structural airframe maintenance. Chinese Journal of Aeronautics, Elsevier, 2019, 33 (3), pp.1242-1257. ⟨hal-02005843⟩



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