Supporting the Implementation of Predictive Maintenance – a Process Reference Model

Carolin Wagner and Bernd Hellingrath
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_21_002.pdf1.36 MBJanuary 12, 2021 - 1:26am

The perception of predictive maintenance as a proactive maintenance strategy to anticipate and reduce severe and costly failures and by thus increasing asset reliability has grown significantly in recent years. Due to the availability of machine sensor data and the intention to use these data in a purposeful way, the introduction of predictive maintenance provides a logical step towards maintenance optimization in industry. Several German industrial surveys highlight the growing interest in the topic by the majority of the addressed companies. Nevertheless, most of these companies are considering predictive maintenance on their future agenda and are currently only at the beginning of its implementation. This is, in many cases, due to missing internal knowledge and systematic guidance for maintenance practitioners. Existing process models and supportive guidance build on theoretical knowledge from experts; however, they often lack the complexity and challenges of industrial applications. In addition, most theoretical models focus on specific aspects of the entire process, target particular application areas, or present a few high-level steps. This paper, therefore, introduces the Process Reference Model for Predictive Maintenance (PReMMa), a comprehensive three-stage hierarchical process reference model for the implementation of predictive maintenance for industrial applications. The process reference model synthesizes existing process models as well as results from interviews with eleven practitioners from both management consultancies and experts from several industrial fields. With regard to four main phases and the predictive maintenance application, results are presented with consideration of the essential steps, their deliverables as well as the involved persons.

Publication Year: 
2021
Publication Volume: 
12
Publication Control Number: 
002
Page Count: 
17
Submission Keywords: 
predictive maintenance
prognostics and health management
Process Reference Model
Industry Insights
Submission Topic Areas: 
Industrial applications
Standards and methodologies
Submitted by: 
  
 
 
 

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