PlatonaM
Platform ecosystem for innovative maintenance management through predictive maintenance
Application industry: Production
Technology area: (Mathematical) optimization and planning; machine learning
PlantonaM is creating the technical prerequisites for the secure and legally compliant use of machine data and thus enabling efficient, predictive maintenance of machines and plants.
The baseline
The systematic, smart analysis of machine data promises considerable efficiency gains in machine maintenance. Studies suggest that predictive maintenance can increase plant uptime by as much as 20 percent, and maintenance cost savings of up to 10 percent are expected. However, this potential has hardly been exploited to date. On the one hand, this is due to widespread concerns regarding secure, legally compliant data use. However, the lack of standardization also has an inhibiting effect: Every manufacturer offers its customers its own data interfaces so that every customer is confronted with many different, manufacturer-specific interfaces. This makes it difficult to comprehensively analyse data from distributed systems, for example, from different machines or components.
The project goal
The goal of PlatonaM is to enable the secure and legally compliant use of digital machine data whilst at the same time respecting data sovereignty and treating data as an independent economic asset. The basis for this is a new type of ‘platform ecosystem’ that reduces the number of data interfaces. Machine data from many manufacturers and customers can thus be systematically combined more easily and analysed with the help of machine learning. The solution allows predictions about machine utilization and provides data-based decision-making aids for maintenance management. As user numbers increase, PlatonaM could become a comprehensive hub for sharing machine data.
Application and practical benefit
PlatonaM is unveiling previously hidden interrelationships in the use of machines and thereby allowing forecasts, for example, about malfunctions and breakdowns, wear-prone components, or maintenance requirements. Maintenance tasks can be better predicted and prioritized. The open architecture of PlatonaM will create a marketplace and a basis on which service providers can offer new, even purely digital business models, for instance, for acquisition, storage, analysis, and visualization of machine data.
Term: 1 January 2019 to 30 June 2022
Consortium: InfAI Managemant GmbH (lead member), Simba n3 GmbH, SITEC Industrietechnologie GmbH, University of Hohenheim, Fraunhofer IML
Contact:
Natanael Arndt
InfAI Management GmbH
E-Mail: arndt@infai.org
Website: www.platona-m.technology.de
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