Structural health monitoring data fusion for in-situ life prognosis of composite structures

A novel framework to fuse structural health monitoring (SHM) data from different in-situ monitoring techniques is proposed aiming to develop a hyper-feature towards more effective prognostics. A state-of-the-art Non-Homogenous Hidden Semi Markov Model (NHHSMM) is utilized to model the damage accumulation of composite structures, subjected to...

Using acoustic emission to understand fatigue crack growth within a single load cycle

Current methods for prediction of fatigue crack growth are based on empirical correlations which do not take the crack growth behaviour within a single cycle into account. To improve these prediction methods, more understanding of the physical mechanisms of crack growth is required. In this research the acoustic emission technique was used to...

Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling

The present study utilizes a state-of-the-art stochastic modeling with structural health monitoring (SHM) data derived from strain measurements, in order to assess the remaining useful life (RUL) online in composite materials under fatigue loading. Non-Homogenous Hidden Semi Markov model (NHHSMM) is a suitable candidate with a rich mathematical...