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.

翻译：结构健康监测（SHM）依赖于声发射（AE）等非破坏性技术，这些技术能在系统生命周期内提供大量数据。此类数据的分析通常基于聚类方法，以获取损伤演化的深层信息。为评估聚类结果，现有方法常采用聚类有效性指标（CVI），这些指标倾向于生成紧凑且分离的簇。然而，这些基于形状的准则并非针对AE数据及SHM领域专门设计。本文提出一种基于簇起始顺序性的新方法。对于监测而言，起始点指示潜在损伤的首次发生时刻，有助于检测缺陷的萌生。所提CVI基于Kullback-Leibler散度，并能在已知损伤先验信息时将其纳入考量。通过三项真实数据集实验验证了该方法的有效性。第一项基准测试涉及振动环境下螺栓连接板松动检测：基于起始点的CVI在簇质量与松动变化检测精度上均优于传统方法。第二项应用针对电火花加工（EDM）中硬质材料的微钻孔工艺：该工业场景证明，所提CVI可用于评估电极推进至参考深度的进程，而这一进程对保障结构完整性至关重要。第三项应用则聚焦复合材料/金属混合连接结构的损伤监测：关键结果表明，基于所提CVI生成的簇时间线可构建出解释滑移导致临界失效的演化场景。