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            • Theses Supervision | Dr. Carolin Wagner
 
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Dr. Carolin Wagner
Former Research Assistant

Chair of Information Systems and Supply Chain Management

Leonardo Campus 3
48149 Münster



ShortURL: ERC.IS ShortURL erc.is/p/wagner

  • About
  • Publications
  • Courses
  • Theses Supervision
  • Supervised Theses

    • Quantitative Comparison of Data-Driven Prognostic Methods - Conception and Implementation of a Unified Methodology (Master's thesis IS, 2021)
    • Applicability of Time Series Clustering in Predictive Maintenance (Bachelor's thesis IS, 2020)
    • Development of a Generic Process for Data Preparation in Predictive Maintenance (Bachelor's thesis IS, 2020)
    • Qualitative assessment of machine learning techniques for fault prognostics in the context of predictive maintenance (Bachelor's thesis IS, 2020)
    • Cost-Benefit Analysis for the Use of Predictive Maintenance (Bachelor's thesis IS, 2019)
    • A Decision Model for the Selection of Fleet-Based Prognostic Methods (Master's thesis IS, 2019)
    • Method selection for machine health prognostics using the example of the steel tube industry: a process for decision support (Master's thesis IS, 2019)
    • The Potential of Bayesian Statistics in PHM for Fleets (Bachelor's thesis IS, 2019)
    • Implementation of Predictive Maintenance – Identification and Analysis of Process Models using Expert Interviews (Bachelor's thesis IS, 2019)
    • Identification and Classification of Approaches for Decision Support in the Field of Predictive Maintenance (Bachelor's thesis IS, 2018)
    • Fleet Fault Diagnostics in Condition-Based Maintenance – Characterization and Assessment of Approaches considering Different Fleet Types (Bachelor's thesis IS, 2017)
    • Development of a Data Analysis Process in the Context of Condition-based Maintenance (Bachelor's thesis IS, 2017)
    • Assessment of Artificial Neural Networks for Predictive Analytics-based Prognostics in Condition-based Maintenance (Master's thesis IS, 2017)
    • Application of machine learning techniques for condition-based maintenance in the context of individual module failure in in-vitro diagnostic instruments (Bachelor's thesis IS, 2017)
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Information Systems and Supply Chain Management
Prof. Dr.-Ing. Bernd Hellingrath

Leonardo-Campus 3
48149 Münster
Germany

Tel.: +49 251 83-38000
Fax: +49 251 83-38009
bernd.hellingrath@ercis.uni-muenster.de

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