Model updating and prognosis of acoustic emission data in compact test specimens under cyclic loading

Acoustic emission (AE) is generated when cracks develop and it is used as an indicator of the current state of damage in structural elements. Algorithms that use AE data to predict the state of a structural element are still in their research stages because the relationship between crack length and AE activity is not well understood. The process of trying to predict the future stage of a crack based on AE data is usually performed by an expert, and requires significant experience. This paper proposes a new strategy for the use of AE data for structural prognosis. A probabilistic model is used to predict AE data. An expert can analyze this data to draw conclusions about the health of the structural member. The goal is to aid the analyst by providing an estimation of the AE activity in the future. The methodology provides the cumulative signal strength at a future number of cycles, assuming the loading and boundary conditions hold. The methodology uses a relationship between the rate of change of the cumulative absolute energy of the AE with respect to the number of cycles and the stress intensity range. A third order polynomial equation that describes the stress intensity range as function of the AE data is proposed. The variables to be updated are treated as random and their joint probability distribution is computed using Bayesian inference. Markov Chain Monte Carlo (MCMC) is used to forecast the cumulative signal strength at some number of cycles in the future. The methodology is tested using a compact test specimen tested in structures lab at the University of South Carolina.