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Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK
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PublicationImprovement of Defect Detectability in Machine Tools Using Sensor-based Condition Monitoring Applications( 2018)
;Demilia, G. ;Gaspari, A. ;Hohwieler, E. ;Laghmouchi, A.Uhlmann, E.The aim of this paper is to present a reliable methodology for condition monitoring of components of high performance centerless grinding machines. This enables the detection and localization of the defects on the ball screw. The fault detection is realized using a self-implemented classification algorithm and other pattern recognition algorithms. The diagnosis is based on acceleration and AE measurement data performed on an axis test rig using various damaged ball screws at different operating parameters. Moreover, the structure of the pattern recognition process will be introduced, this includes the signal pre-processing, the selection of the most suitable features for this specific application using MATLAB®. Finally, the evaluation of the developed solution will be showed. The actions allowing improving the accuracy of measurement data and the effectiveness of the processing algorithms, based on MATLAB® applications, are described throughout the paper. Satisfactory classification results will be obtained and discussed. -
PublicationSmart wireless sensor network and configuration of algorithms for condition monitoring applications( 2017)
;Uhlmann, E. ;Laghmouchi, A. ;Geisert, C.Hohwieler, E.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 S ystems) 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. -
PublicationIntelligent pattern recognition of SLM machine energy data( 2017)
;Uhlmann, E. ;Pastl Pontes, R. ;Laghmouchi, A. ;Hohwieler, E.Feitscher, R.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. -
PublicationZustandsüberwachung in der Cloud( 2016)
;Uhlmann, E. ;Laghmouchi, A. ;Hohwieler, E.Geisert, C.Aufgrund der hohen Verfügbarkeitsanforderungen an Produktionsmaschinen wächst das Interesse an zustandsbasierter Instandhaltung. Der Einsatz von Zustandsüberwachungssystemen (Condition Monitoring-Systemen) zur Steigerung der Verfügbarkeit von Maschinen und zur Reduktion der Instandhaltungskosten spielt dabei eine entscheidende Rolle und hat in den letzten Jahren zugenommen. Da am Markt verfügbare und auf Industriesensoren basierende Lösungen meist anwendungsspezifisch, kostenintensiv und in der Inbetriebnahme aufwändig sind, wurde am Fraunhofer IPK ein Konzept für die Zustandsüberwachung in der Cloud entwickelt, das mithilfe von Einplatinen-Computern und MEMS-Beschleunigungssensoren (Mikro-Elektro-Mechanisches-System) als Sensorknoten eine preisgünstige und einfach zu handhabende Alternative darstellt. -
PublicationIntelligentes Elektroniksystem für Condition Monitoring in Industrie 4.0( 2016)
;Uhlmann, E. ;Laghmouchi, A. ;Ehrenpfordt, R. ;Hohwieler, E.Geisert, C.Im Rahmen dieses Beitrags werden die geplanten Arbeiten des Forschungsprojekts ""AMELI4.0"" vorgestellt. Der Schwerpunkt dieses Projekts liegt in der Entwicklung und Umsetzung hochintegrierter, vernetzter, energieautarker MEMS-Multisensorsysteme (Mikro-Elektro-Mechanische Systeme - MEMS) mit intelligenter Echtzeit-Datenverarbeitung auf Sensorebene bei hoher Daten- und Systemsicherheit. Das Multisensorsystem integriert mehrere MEMS-Sensoren zur Erfassung von Körperschall und akustischer Schall in Kombination mit der energieeffizienten Signalvorverarbeitung auf Sensorebene (Smart Data statt Big Data) bei hoher Systemrobustheit in einem modularen Hardware- und Plattformdesign. Des Weiteren werden die adressierten Anwendungsfälle und der Forschungsschwerpunkt des Fraunhofer IPK zum Thema Datenanalyse und Datenmanagement vorgestellt. -
PublicationCondition monitoring in the cloud( 2015)
;Uhlmann, E. ;Laghmouchi, A. ;Hohwieler, E.Geisert, C.Due to the very high demands on availability and efficiency of production systems and industrial systems, condition-based maintenance is becoming increasingly important. The use of condition monitoring approaches to increase the machine availability and reduce the maintenance costs, as well as to enhance the process quality, has increased over the last years. The installation of industrial sensors for condition monitoring reasons is complex and cost-intensive. Moreover, the condition monitoring systems available on the market are application specific and expensive. The aim of this paper is to present the concept of a wireless sensor network using Micro-Electro-Mechanical Systems - MEMS sensors and Raspberry Pi 2 for data acquisition and signal processing and classification. Moreover, its use for condition monitoring applications and the selected and implemented algorithm will be introduced. This concept realized by Fraunhofer Institute for Production Systems and Design Technology IPK, can be used to detect faults in wear-susceptible rotating components in production systems. It can be easily adapted to different specific applications because of decentralized data preprocessing on the sensor nodes and pool of data and services in the cloud. A concrete example for an industrial application of this concept will be represented. This will include the visualization of results which were achieved. Finally, the evaluation and testing of this concept including. implemented algorithms on an axis test rig at different operation parameters will be illustrated.