Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Decentralized data analytics for maintenance in industrie 4.0
    ( 2017)
    Uhlmann, Eckart
    ;
    Laghmouchi, Abdelhakim
    ;
    Geisert, Claudio
    ;
    Hohwieler, Eckhard
    Due to the increased digital networking of machines and systems in the production area, large datasets are generated. In addition, more external sensors are installed at production systems to acquire data for production and maintenance optimization purposes. Therefore, data analytics and interpretation is one of the challenges in Industrie 4.0 applications. Reliable analysis of data (e.g. internal and external sensors), such as system-internal alarms and messages produced during the operation, can be used to optimize production and maintenance processes. Furthermore, information and knowledge can be extracted from raw data and used to develop data-driven business models and services, e.g. offer new availability contracts for production systems. This paper illustrates an approach for decentralized data analytics based on smart sensor networks.
  • Publication
    Smart life cycle monitoring for sustainable maintenance and production
    ( 2017)
    Uhlmann, Eckart
    ;
    Pastl Pontes, Rodrigo
    ;
    Laghmouchi, Abdelhakim
    ;
    Geisert, Claudio
    ;
    Hohwieler, Eckhard
    Smart linking, evaluation and provision of information over the life cycle of a product are becoming growingly important. The use of information extracted from combination of monitoring data, product data, maintenance information, and from product utilisation data can increase the availability of production machines and reduce the costs and resources cause by machine downtime. Especially for new manufacturing technologies such as Selective Laser Melting, the storage and management of such information are crucially important to develop knowledge and improve the quality of the machines and their products. By acquiring data from the machine, processing them and calculating proper key performance indicators, the critical region where the failures are most commonly found and the critical subsystems responsible for the failures are identified. Moreover, using the historical data, the tolerances for those subsystems can be defined.
  • Publication