Clustering acoustic emission data streams with sequentially appearing clusters using mixture models
This work addresses the specific challenge of interpreting unlabeled acoustic emission data for structural health monitoring applications, representing an incremental improvement with a novel objective function tailored to AE data characteristics.
The authors tackled the problem of clustering acoustic emission data streams with sequentially appearing clusters by developing GMMSEQ, a modified Gaussian Mixture Model that incorporates cluster onsets, kinetics, and activation levels through time. The method outperformed three standard clustering methods on raw streaming data from five experimental campaigns, providing better cluster characterization and useful qualitative timeline information.
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.