Structure analysis and de-noising using Singular Spectrum Analysis: Application to acoustic emission signals from nuclear safety experiments

We explore the abilities of the Singular Spectrum Analysis (SSA) to characterize and de-noise discrete acoustic emission signals. The method is first tested on simulated data for which different types and levels of noise are considered. It is then applied on real data recorded from nuclear safety experiments. The results show an excellent ability of the SSA to characterize the corrupted signal and to detect structural changes, even for low signal-to-noise ratio. For de-noising purposes, the quality of the results depends mainly on the separability between the source signal to be estimated and the noise. However, whatever the case, the main components of the source signal are clearly identified when the components associated with the noise are removed. (authors)