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Experimentelle Untersuchungen am Bondautomaten Bildinformationen anzeigen
Qualitätsbeurteilung von Kupferbondverbindungen am Schertester Bildinformationen anzeigen
Verlässlichkeitsanalyse an einer Reibkupplung Bildinformationen anzeigen
Schwingungsmessung und -analyse in der Lehre Bildinformationen anzeigen
Transport feiner Pulver mittels Ultraschall Bildinformationen anzeigen

Experimentelle Untersuchungen am Bondautomaten

Qualitätsbeurteilung von Kupferbondverbindungen am Schertester

Verlässlichkeitsanalyse an einer Reibkupplung

Schwingungsmessung und -analyse in der Lehre

Transport feiner Pulver mittels Ultraschall

Mitarbeiter des Lehrstuhls für Dynamik und Mechatronik

Meike Wohlleben

 Meike  Wohlleben

Lehrstuhl für Dynamik und Mechatronik (LDM)

Wissenschaftliche Mitarbeiterin - Hybride Modellierung

+49 5251 60-1810
+49 5251 60-1803

Termin nach Vereinbarung

Pohlweg 47-49
33098 Paderborn

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Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction

M.C. Wohlleben, A. Bender, S. Peitz, W. Sextro, in: Machine Learning, Optimization, and Data Science, Springer International Publishing, 2022



Rule-based Diagnostics of a Production Line

O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527-536

In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.

Liste im Research Information System öffnen

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