Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Daten sind eine wichtige Grundlage für die Anwendung von maschinellem Lernen (ML) in der Industrie, zum Beispiel für die Zustandsbewertung in der Montage. Insbesondere bei Brownfield-Anlagen ist die Datenqualität für einen zuverlässigen Einsatz von ML-Methoden häufig nicht ausreichend. Im Rahmen dieses Beitrags wird eine open-source Checkliste, basierend auf dem CRISP-DM Referenzmodell, vorgestellt, die die kritischen Punkte bei ML-Projekten abdeckt und so die Datenqualität sicherstellen und steigern soll.
Data is an important basis for the application of machine learning (ML) in industry, e.g., for condition monitoring in assembly lines. Especially in brownfield systems, the data quality is often not sufficient for a reliable application of ML algorithms. This paper presents an open-source checklist, based on the established CRISP-DM reference model, covering critical points in machine learning projects to ensure data quality.
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