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
Carolin Wagner
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Carolin Wagner, 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 (2022), Logos Verlag, Berlin, ISBN: 9783832584511
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Description / Abstract
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
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
Description
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.
Table of content
- BEGINN
- 1 Introduction
- 1.1 Motivation and Problem Statement
- 1.2 Research Objective
- 1.3 Research Design
- 1.4 Thesis Structure
- 2 Fundamentals of Predictive Maintenance
- 2.1 Maintenance Management
- 2.2 Predictive Maintenance
- 2.3 Methods for Data-Driven Prognostics
- 2.4 Current Research Gaps in Predictive Maintenance
- 3 Process Reference Model for Predictive Maintenance
- 3.1 Requirements for the Process Reference Model
- 3.2 Methodological Approach
- 3.3 Information Gathering
- 3.4 Process Reference Model Design and Construction
- 3.5 Process Reference Model Evaluation
- 3.6 Discussion and Limitations
- 4 Characterization Method for Fleet Prognostics
- 4.1 Objectives and Requirements Definition for the Characterization Method
- 4.2 Methodological Approach
- 4.3 Fleet Conceptualization for Prognostics
- 4.4 Characterization Method Design and Construction
- 4.5 Evaluation of the Characterization Method
- 4.6 Discussion and Limitations
- 5 Algorithm Development Method for Fleet Prognostics
- 5.1 Objectives and Requirements Definition for the Algorithm Development Method
- 5.2 Methodological Approach
- 5.3 Information Gathering
- 5.4 Algorithm Development Method Design and Construction
- 5.5 Evaluation of the Algorithm Development Method
- 5.6 Discussion and Limitations
- 6 Practical Evaluation with Three Industrial Cases
- 6.1 Methodological Approach
- 6.2 Case A: Automotive Supplier
- 6.3 Case B: Commercial Vehicle Manufacturer
- 6.4 Case C: Agricultural Machinery Manufacturer
- 6.5 Practical Evaluation and Discussion
- 7 Conclusion
- 7.1 Summary
- 7.2 Limitations
- 7.3 Outlook
- References
- Appendix