Real time damage state estimation and condition based residual useful life estimation of a metallic specimen under biaxial loading

The current state of the art in the area of real time structural health monitoring techniques offers adaptive damage state prediction and residual useful life assessment. The present paper discusses the use of an integrated prognosis model, which combines an on-line state estimation model with an off-line predictive model to adaptively estimate the residual useful life of an Al-6061 cruciform specimen under biaxial loading. The overall fatigue process is assumed to be a slow time scale process compared to the time scale at which, the sensor signals were acquired for on-line state estimation. The on-line state estimation model was based on correlation analysis, which is a type of non-parametric system identification approach. A new damage index is proposed, which is proportional to the cumulative damage state of the structure at any particular fatigue cycle. The on-line model regularly estimates the current damage state of the structure based on passive strain gauge signals. These damage states were used to update the slow scale off-line predictive model as it becomes available. The off-line predictive model is a probabilistic nonlinear regression model, which is based on a Bayesian statistics based Gaussian process approach. The off-line module adaptively updates the model parameters and recursively predicts the future states to provide real time residual useful life estimate.