PriMa: a prescriptive maintenance model for cyber-physical production systems

ABSTRACT

Cyber-physical production systems (CPPS), as an emerging Industry 4.0’s technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined.

KEYWORDS:

• Maintenance
• prescriptive analytics
• predictive analytics
• production planning
• CPPS
• smart factory

Acknowledgments

The authors would like to acknowledge the financial support of the European Commission provided through the Centre of Excellence in Production Informatics and Control (EPIC). The project has received funding from the European Union's Horizon 2020 research and innovation programme under the grant No. 739592. The industrial case studies mentioned in this paper have been carried out within the research project “Maintenance 4.0” (2014–2017), funded by the Austrian Research Promotion Agency (FFG) under the grant number 843668. In addition, the authors acknowledge the TU Wien University Library for financial support through its Open Access Funding Program.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Horizon 2020 Framework Programme [739592]; Austrian Research Promotion Agency (FFG) [843668].