Toward a probabilistic acoustic emission source location algorithm: A Bayesian approach

Abstract

Acoustic emissions (AE) are stress waves initiated by sudden strain releases within a solid body. These can be caused by internal mechanisms such as crack opening or propagation, crushing, or rubbing of crack surfaces. One application for the AE technique in the field of Structural Engineering is Structural Health Monitoring (SHM). With piezo-electric sensors mounted to the surface of the structure, stress waves can be detected, recorded, and stored for later analysis. An important step in quantitative AE analysis is the estimation of the stress wave source locations. Commonly, source location results are presented in a rather deterministic manner as spatial and temporal points, excluding information about uncertainties and errors. Due to variability in the material properties and uncertainty in the mathematical model, measures of uncertainty are needed beyond best-fit point solutions for source locations. This paper introduces a novel holistic framework for the development of a probabilistic source location algorithm. Bayesian analysis methods with Markov Chain Monte Carlo (MCMC) simulation are employed where all source location parameters are described with posterior probability density functions (PDFs). The proposed methodology is applied to an example employing data collected from a realistic section of a reinforced concrete bridge column. The selected approach is general and has the advantage that it can be extended and refined efficiently. Results are discussed and future steps to improve the algorithm are suggested.