Short title: Maintenance 4.0
Long title: Maintenance 4.0 - Assurance of product quality and plant availability through a real-time based maintenance control center
Sponsors: Österreichische Forschungsförderungsgesellschaft - FFG
IMW contribution to the research project:
- Requirement analysis and analysis of existing systems
- Identification of correlations between the input data
- Development of a reaction model with an underlying set of rules
- The conception of the anticipatory maintenance strategy
- Project management and dissemination
In the research project "Instandhaltung 4.0", the project partners under the consortium leadership of TU Wien have investigated the development of an innovative maintenance control center, which predicts downtimes and proposes anticipatory maintenance measures. For this purpose, multiple data sources have been linked together - real-time machine control data, condition monitoring data, quality measurement data of the products and the historical knowledge about failure events. A reaction model running in the background combines the condition and load-dependent service life calculation with a statistical failure behavior of the plant under consideration.
The results of the Instandhaltung 4.0 research project enable more precise forecasting of the time of failure of production machines and a predictive maintenance strategy. The core of this project was the development of an innovative maintenance control center. Within the scope of the project, a potential reduction in downtimes of up to 25% was achieved, depending on the machine component. The improved planning capability increases the process stability on the entire shop floor, and a significant step towards "Maintenance 4.0" has been taken.
The maintenance control center offers:
- Presentation of mechanical wear and tear
- Presentation of discrepancies in the quality data
- Measures to support decision-making
- KPI cockpit for plant-specific evaluation
- Visualization of the evaluation
The mobile maintenance app offers:
- Presentation of the state of the plant (current operating state, current consumption etc.)
- Role-specific definable KPI cockpit
- Rapid recording and low-cost management of incidents
- A detailed evaluation of the OEE
- TU Wien Institut für Managementwissenschaften
- TU Wien Institut für Fertigungstechnik und Photonische Technologien
- Fraunhofer Austria Research GmbH
- Montanuniversität Leoben Lehrstuhl für Wirtschafts- und Betriebswissenschaften
Opel Wien GmbH
Dipl.-Ing. Robert Glawar
- F. Ansari, R. Glawar & T. Nemeth, PriMa: A Prescriptive Maintenance Model for Cyber-Physical Production Systems, International Journal of Computer Integrated Manufacturing, Vol. 32, Issue 4-5: Smart Cyber-Physical System Applications in Production and Logistics, Taylor & Francis, 2019, pp. 482-503
- T. Nemeth, F. Ansari, W. Sihn, B. Haslhofer & A. Schindler, PriMa-X: A Reference Model for Realizing Prescriptive Maintenance and Assessing its Maturity Enhanced by Machine Learning, Procedia CIRP, Vol. 72, 2018, pp. 1039-1044.
- F. Ansari & R. Glawar, Knowledge-Based Maintenance, Book Chapter, In Instandhaltungslogistik, K. Matyas (Ed.), 7th Edition, Carl Hanser Verlag, 12/2018, pp. 318-342.
- K. Matyas, T. Nemeth, K. Kovacs, R. Glawar, 2017: A procedural approach for realizing prescriptive maintenance planning in manufacturing industries.
- R. Glawar , Z. Kemeny, T. Nemeth et.al., 2016: A Holistic Approach for Quality Oriented Maintenance Planning Supported by Data Mining Method
- R. Glawar, C. Habersohn, T. Nemeth et.al. 2016: A holistic approach for anticipative maintenance planning supported by a dynamic calculation of wear reserve
- T. Nemeth, R. Bernerstätter, R. Glawar, et.al.,2015: Instandhaltung 4.0: Sicherstellung von Produktqualität und Anlagenverfügbarkeit durch einen echtzeitbasierten Instandhaltungsleitstand