Monte Carlo-based filtering for fatigue crack growth estimation

The lifetime prediction of industrial and structural components is a recognized valuable task for operating safely and managing with profit the production of industrial plants. One way to address this prognostic challenge is by implementing model-based estimation methods for inferring the life evolution of a component on the basis of a sequence of noisy measurements related to its state. In practice, the non-linearity of the state evolution and/or the non-Gaussianity of the associated noise may lead to inaccurate prognostic estimations even with advanced approaches, such as the Kalman, Gaussian-sum and grid-based filters. An alternative approach which seems to offer significant potential of successful application is one which makes use of Monte Carlo-based estimation methods, also called particle filters. The present paper demonstrates such potential on a problem of crack propagation under uncertain monitoring conditions. The crack growth process, taken from literature, is described by a non-linear model affected by non-additive noises. To the authors’ best knowledge, this is the first time that (i) a particle filtering technique is applied to a structural prognostic problem and (ii) the filter is modified so as to estimate the distribution of the component’s remaining lifetime on the basis of observations taken at predefined inspection times.