Machine Learning in Defectoscopy: DBSCAN for Localization, Agglomerative Clustering for Typification

Acoustic emission (AE) is one of the most relevant and cost-effective non-destructive testing methods for identifying defects in industrial objects. This paper addresses two primary tasks in defectoscopy: locating hazardous AE zones and classifying signals according to the presumed defect type. Through our research, we have determined and experimentally confirmed that the most suitable machine learning method for localization is a modified DBSCAN, while Agglomerative Clustering is optimal for typification. This paper details the specific tuning and modifications applied to these methods for AE. The resulting models were successfully tested on real-world data. The accuracy of the localization results was assessed using statistical analysis and consultations with AE experts, while typification was validated probabilistically and tested on sample data.