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Wyszukujesz frazę "Laghmouchi, A." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Smart wireless sensor network and configuration of algorithms for condition monitoring applications
Autorzy:
Uhlmann, E.
Laghmouchi, A.
Geisert, C.
Hohwieler, E.
Powiązania:
https://bibliotekanauki.pl/articles/99644.pdf
Data publikacji:
2017
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
condition monitoring
data analysis
sensor network
algorithm
MEMS sensor
cloud
Opis:
Due to high demand on availability of production systems, condition monitoring is increasingly important. In recent years, the technical development have improved for realization of condition monitoring applications as a result of technological progress in fields such as sensor technology, computer performance and communication technology. Especially, the approaches of Industrie 4.0 and the use of the Internet of Things (IoT) technologies offer high potential to implement condition monitoring solutions. The connection of several sensor data of components to the cloud allows the identification of anomalies or defect pattern, this information can be used for predictive maintenance and new data-driven business models in production industry. This paper illustrates a concept of a smart wireless sensor network for condition monitoring application based on simple electronic components such as the single-board computer Raspberry Pi 2 modules and MEMS (Micro-Electro-Mechanical Systems) vibration sensors and communication standards MQTT (Message Queue Telemetry Transport). The communication architecture used for decentralized data analysis using machine learning algorithms and connection to the cloud is explained. Furthermore, a procedure for rapid configuration of condition monitoring algorithms to classify the current condition of the component is demonstrated.
Źródło:
Journal of Machine Engineering; 2017, 17, 2; 45-55
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent pattern recognition of SLM machine energy data
Autorzy:
Uhlmann, E.
Pastl Pontes, R.
Laghmouchi, A.
Hohwieler, E.
Feitscher, R.
Powiązania:
https://bibliotekanauki.pl/articles/99469.pdf
Data publikacji:
2017
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
pattern recognition
data analysis
additive manufacturing
energy data
Opis:
Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures.
Źródło:
Journal of Machine Engineering; 2017, 17, 2; 65-76
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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