  The interpretation of unlabeled acoustic emission (AE) data classically
relies on general-purpose clustering methods. While several external criteria
have been used in the past to select the hyperparameters of those algorithms,
few studies have paid attention to the development of dedicated objective
functions in clustering methods able to cope with the specificities of AE data.
We investigate how to explicitly represent clusters onsets in mixture models in
general, and in Gaussian Mixture Models (GMM) in particular. By modifying the
internal criterion of such models, we propose the first clustering method able
to provide, through parameters estimated by an expectation-maximization
procedure, information about when clusters occur (onsets), how they grow
(kinetics) and their level of activation through time. This new objective
function accommodates continuous timestamps of AE signals and, thus, their
order of occurrence. The method, called GMMSEQ, is experimentally validated to
characterize the loosening phenomenon in bolted structure under vibrations. A
comparison with three standard clustering methods on raw streaming data from
five experimental campaigns shows that GMMSEQ not only provides useful
qualitative information about the timeline of clusters, but also shows better
performance in terms of cluster characterization. In view of developing an open
acoustic emission initiative and according to the FAIR principles, the datasets
and the codes are made available to reproduce the research of this paper.
