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Reliable self-learning production systems based on contextual services

The strategic goal is to strengthen the EU’s leadership in production technologies in the global market by developing innovative self-learning solutions that enable tight integration of control and maintenance of production systems. The project will develop highly reliable and secure service-based self-learning solutions aimed at this integration. The methodology addressing the organizational aspects of such a radical change in production systems will be elaborated in the context of an extended enterprise approach using lean principles. Approaches based on SOA principles and using distributed, networked, embedded services in the device domain are best suited for implementing such self-learning solutions. The main obstacles to fully exploiting the opportunities offered by service-based self-learning systems are issues related to reliability, availability, interoperability, and security and trust in various SW services. Context awareness, which provides information about the processes, equipment, and circumstances under which services operate and enables them to respond accordingly, is a promising holistic approach to provide the required self-learning adaptation to changes in processes and equipment state and, on the other hand, the quality of service required for such self-learning production systems. The project will develop context models and a self-learning context extractor, self-learning adapters for control, maintenance and parameter identification, and an SOA-based infrastructure including a security and trust framework and agent-based middleware that will be introduced into the service structure. The project is driven by three industrial application scenarios from three industrial partners using self-learning components and SOA infrastructure for integrated control and maintenance for machine tools, FMS and assembly lines in discrete manufacturing. The proposed approach will result in a 12% reduction in maintenance costs and a 10% increase in overall equipment effectiveness.

More information under: https://cordis.europa.eu/article/id/91150-selflearning-adaptive-production-systems

 

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