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
  • Publication
    Data mining and visualization of diagnostic messages for condition monitoring
    ( 2013)
    Uhlmann, Eckart
    ;
    Geisert, Claudio
    ;
    Hohwieler, Eckhard
    ;
    Altmann, Ian
    Complex technical systems consist of a huge amount of electromechanical subsystems and components with more or less interdependencies. During the operational phase these systems are subject to wear and tear and other degradation mechanisms. Therefore condition monitoring is a major challenge for operators of such systems. A well-established practice of implementing condition monitoring is to equip the system, or its functionally relevant components with appropriate sensors and measurement technology. Selection of sensors and diagnostic algorithms requires a deep understanding of physical correlations which is usually a core competence of the system provider. Moreover, subsequent installation of a condition monitoring system using additional sensors in most cases is attended by an interference with the system and may affect its functionality if constructional changes of the system for sensor mounting become necessary. A more simple way to access data for technical diagnosis is the use of an on board diagnosis system that collects the system messages that rise while the system is in operation. To control and monitor functionality and interaction between subsystems, each of them generates internal state variables and communicates a subset of these as system messages via task specific bus systems. Here the challenge is to find a suitable way of analyzing the messages. The present paper discusses how to handle system messages for condition monitoring purposes by using data mining and visualization.
  • Publication
    Lifecycle-monitoring. Integration in Service-Prozesse und Produktdatenmanagement
    ( 2012)
    Uhlmann, Eckart
    ;
    Geisert, Claudio
    ;
    Hohwieler, Eckhard
    Der vorliegende Beitrag beschreibt zunächst, aus welchen Gründen ein Lifecycle-Monitoring notwendig ist. Er zeigt auf, welche prinzipiellen Daten im Rahmen eines Lifecycle-Monitoring benötigt werden, wie sie erfasst, archiviert und sinnvoll verknüpft werden können. Die Notwendigkeit der informationstechnischen Verknüpfung der in einer heterogenen Systemlandschaft vorliegenden Informationen wird abschließend an einem kurzen Praxisbeispiel erläutert.