Prediction of future health state of an aerospace structure allows airlines to plan repair or replacement activities well in time. Correct planning ensures low probability of failure as well as low aircraft's downtime. Accurate prediction is possible through usage of the effective prognostic algorithms, based on historical trends and underlying suitable degradation models. A particle filter (PF)-based prognostic algorithm is proposed to predict posterior probability density function of flaw size in future. PF allows usage of nonlinear state-transition and measurement functions, along with non-Gaussian/multimodal noise distributions. The proposed algorithm is further complemented by a learning-based error model to estimate associated noises and increase prediction accuracy once measurements are not available. The proposed algorithm is applied on actual aerospace historical data of an in-service Airbus A310 aircraft. The historical data is divided into training phase and validation phase. A common aerospace structural flaw due to mechanical fatigue is the damage around the edges of countersunk (CSK) holes of an aircraft wing. This study is based on three-dimensional flaw growth around the CSK rivet holes. Accurate prediction of flaw propagation will enable the maintenance managers to plan repair or replacement action well in time, thus reducing the downtime of the aircraft.

A study of behaviour patterns associated with students accessing an online discussion forum is presented. Data collected on the frequency of access and the duration of sessions is analysed to establish several categories of learners, which depict the differences among the cohort in terms of participation in social learning. A British business school course for second year undergraduates was studied over two years (i.e. two cohorts) and the results were combined to derive categories of learner types. We conclude that that academic attainment does not appear to be related to student access behaviour necessarily.