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. (C) 2008 Elsevier Ltd. All rights reserved.