Structural Health Monitoring (SHM) relies on non-destructive techniques such as Acoustic Emission (AE) which provide a large amount of data over the life of the systems. The analysis of these data is often based on clustering in order to get insights about damage evolution. In order to evaluate clustering results, current approaches include Clustering Validity Indices (CVI) which favor compact and separable clusters. However, these shape-based criteria are not specific to AE data and SHM. This paper proposes a new approach based on the sequentiality of clusters onsets. For monitoring purposes, onsets indicate when potential damage occurs for the first time and allows to detect the inititation of the defects. The proposed CVI relies on the Kullback-Leibler divergence and enables to incorporate prior on damage onsets when available. Three experiments on real-world data sets demonstrate the relevance of the proposed approach. The first benchmark concerns the detection of the loosening of bolted plates under vibration. The proposed onset-based CVI outperforms the standard approach in terms of both cluster quality and accuracy in detecting changes in loosening. The second application involves micro-drilling of hard materials using Electrical Discharge Machining. In this industrial application, it is demonstrated that the proposed CVI can be used to evaluate the electrode progression until the reference depth which is essential to ensure structural integrity. Lastly, the third application is about the damage monitoring in a composite/metal hybrid joint structure. As an important result, the timeline of clusters generated by the proposed CVI is used to draw a scenario that accounts for the occurrence of slippage leading to a critical failure.

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