Reliability-based optimization of maintenance scheduling of mechanical components under fatigue.

This study presents the optimization of the maintenance scheduling of mechanical components under fatigue loading. The cracks of damaged structures may be detected during non-destructive inspection and subsequently repaired. Fatigue crack initiation and growth show inherent variability, and as well the outcome of inspection activities. The problem is addressed under the framework of reliability based optimization. The initiation and propagation of fatigue cracks are efficiently modeled using cohesive zone elements. The applicability of the method is demonstrated by a numerical example, which involves a plate with two holes subject to alternating stress.

Modeling of the variability of fatigue crack growth using cohesive zone elements.

By its nature, metal fatigue has random characteristics, leading to extensive scatter in the results. Both initiation and propagation of a fatigue crack can be seen as random processes. This manuscript develops a numerical analysis using cohesive zone elements allowing the use of one single model in the finite element simulation of the complete fatigue life. The present formulation includes a damage evolution mechanism that reflects gradual degradation of the cohesive strength under cyclic loading. The uncertainties inherent to the fatigue process are assumed to be caused by the variability of the material properties, which are modeled using random fields. An extrapolation scheme is proposed to reduce the computational time. First, the accuracy of the proposed formulation is assessed considering a deterministic crack growth problem. Second, the effect of randomness in the material properties on the total fatigue life of a structure is then analyzed.

Investigation of model uncertainties in Bayesian structural model updating.

Model updating procedures are applied in order to improve the matching between experimental data and corresponding model output. The updated, i.e. improved, finite element (FE) model can be used for more reliable predictions of the structural performance in the target mechanical environment. The discrepancies between the output of the FE-model and the results of tests are due to the uncertainties that are involved in the modeling process. These uncertainties concern the structural parameters, measurement errors, the incompleteness of the test data and also the FE-model itself. The latter type of uncertainties is often referred to as model uncertainties and is caused by simplifications of the real structure that are made in order to reduce the complexity of reality. Several approaches have been proposed for taking model uncertainties into consideration, where the focus of this manuscript will be set on the updating procedure within the Bayesian statistical framework. A numerical example involving different degrees of nonlinearity will be used for demonstrating how this type of uncertainty is considered within the Bayesian updating procedure.