Parameters Influencing Moisture Diffusion in Epoxy-Based Materials during Hygrothermal Ageing-A Review by Statistical Analysis.

The hygrothermal ageing of epoxy resins and epoxy matrix composite materials has been studied many times in the literature. Models have been developed to represent the diffusion behaviour of the materials. For reversible diffusions, Fick, Dual-Fick and Carter and Kibler models are widely used. Many parameters, correlated or not, have been identified. The objectives of this review by statistical analysis are to confirm or infirm these correlations, to highlight other correlations if they exist, and to establish which are the most important to study. This study focuses on the parameters of the Fick, Dual-Fick and Carter and Kibler models. For this purpose, statistical analyses are performed on data extracted and calculated from individuals described in the literature. Box plot and PCA analyses were chosen. Differences are then noticeable according to the different qualitative parameters chosen in the study. Moreover, correlations, already observed in the literature for quantitative variables, are confirmed. On the other hand, differences appear which may suggest that the models used are inappropriate for certain materials.

Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects.

Failure prognostics has become a central element in predictive maintenance. In this domain, the accurate determination of the remaining useful life (RUL) allows making effective maintenance and operation decisions about the assets. However, prognostics is often approached from a component point of view, and system-level prognostics, taking into account component interactions and mission profile effects, is still an underexplored area. To address this issue, we propose an online joint estimation and prediction methodology using a modeling framework based on the inoperability input-output model (IIM). This model can consider the interactions between components and also the mission profile effects on a system's degradation. To estimate the system's parameters in real-time, with a minimum of prior knowledge, an online estimation process based on the gradient descend algorithm is recursively performed when acquiring new measurements. After each update, the estimated model is used to predict the system RUL. The performance of the proposed approach is highlighted through different numerical examples. In addition, these developments are applied to a real industrial application, the Tennessee Eastman Process, in order to show their effectiveness.