Model-based prognosis of fatigue crack growth under variable amplitude loading

Abstract

In this paper, a model-based prognosis method using a particle filter that takes model uncertainty, measurement uncertainty and future loading uncertainty into account is proposed. First of all, a nonlinear analytical model of the degradation that depends on loading inputs is established. Then, a joint estimation of the unknown loading inputs and of the degradation state is performed with a particle filter. Moreover, a two-sided cumulative sum (CUSUM) algorithm is implemented to detect abrupt load variations and help the particle filter to adapt and learn new loading inputs values. With the combination of these two techniques, the prognosis module could be informed of the sudden crack length increase, and will correct the predicted remaining useful life. Real data from fatigue tests on fiber-reinforced metal matrix composite materials are used to demonstrate the efficiency of the proposed methodology for crack growth prognosis.