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- Conference Proceeding (3) (remove)
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- 2021 (3)
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- Industrie 4.0 (3) (remove)
Reducing costs is an important part in todays business. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.
Die Zustandsüberwachung (Condition Monitoring) und die vorausschauende Wartung (Predicted Maintenance) gelten als Schlüsselinnovationen der Industrie 4.0. Im Zuge dessen arbeiten Forscher der Professur Intelligente
Maschinensysteme an einem Kunststoffgleitlager, welches eine integrierte Sensorik besitzt, die dem Anwender die Überwachung von Betriebsdaten ermöglichen soll. Die aus einem elektrisch leitfähigen Kunststoff hergestellte Sensorik trägt dazu bei, dass in Echtzeit sowohl eine Aussage zur Lagertemperatur als auch zum Verschleißgrad des Lagers getroffen werden kann.
Damit lassen sich Wartungsintervalle besser planen und ein
prophylaktischer Austausch von noch gebrauchsfähigen Lagern kann vermieden und damit Kosten gesenkt werden. Dafür sind umfangreiche numerische Untersuchungen zum mechanischen, thermischen und elektrischen Verhalten des Gleitlagers durchgeführt worden. Außerdem sind die Auswerteelektronik und Werkstoffe entsprechend den Anforderungen der Sensorik entwickelt bzw. ausgewählt worden.
Over the last two decades, the rapid advances in digitization methods put us on the fourth industrial era’s cusp. It is an era of connectivity and interactivity between various industrial processes that need a new, trusted environment to exchange and share information and data without relying on third parties. Blockchain technologies can provide such a trusted environment. This paper focuses on utilizing the blockchain with its characteristics to build machine-to-machine (M2M) communication and digital twin solutions. We propose a conceptual design for a system that uses smart contracts to construct digital twins for machines and products and executes manufacturing processes inside the blockchain. Our solution also employs the decentralized identifiers standard (DIDs) to provide self-sovereign digital identities for machines and products. To validate the approach and demonstrate its applicability, the paper presents an actual implementation of the proposed design to a simulated case study done with the help of Fischertechnik factory model.