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A Process-Centric View on Predictive Maintenance and Fleet Prognostics. Development of a Process Reference Model and a Development Method for Fleet Prognostics to Guide Predictive Maintenance Projects

Advances in Information Systems and Management Science, Bd. 65

Carolin Wagner

ISBN 978-3-8325-5515-3
305 pages, year of publication: 2022
price: 47.50 €
In the age of digitalization and the fourth industrial revolution, predictive maintenance is becoming increasingly important as a proactive maintenance type. Despite the economic benefits that predictive maintenance generates for companies, its practical application is still in its early stages. This is often due to two prevailing challenges. First, there is a deficiency of knowledge about predictive maintenance and its concrete realization. Second, there is a lack of high quality and rich data of historical machine failures. To increase the representativeness of data, data from several similar machines (i.e. a fleet) should be considered.

To foster the effective implementation of predictive maintenance, supportive guidance in the realization of a predictive maintenance project is needed. For this reason, this dissertation presents a process reference model and a development method for fleet prognostics. The process reference model describes a comprehensive and application-independent view of the complete predictive maintenance process. The model is supplemented by the fleet prognostic development method. To address the specific characteristics of the fleet, a systematic process is depicted which provides a means to assess the heterogeneity of the fleet from a data-driven perspective and simplifies the design of an algorithm considering fleet data. Finally, the applicability and value of the research results are demonstrated with three industrial cases

Carolin Wagner studied Information Systems at the University of Münster, Germany. Afterwards, she worked as a research assistant at the European Research Center for Information Systems (ERCIS). During this time, she conducted research in the field of predictive maintenance at the Chair of Information Systems and Supply Chain Management. In July 2021, she received her doctorate in economics.

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Keywords:
  • Predictive Maintenance
  • Process Reference Model
  • Machine Learning
  • Fleet
  • Prognostics

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