Data Engineering for industry 4.0

Data Engineering for industry 4.0

Within industry 4.0 data is playing a pivotal role, in this field we

  • Develop and apply black boc machine (deep) learning tecniques, including machine learning and semantics to include expert knowledge to obtain interpretable outputs;
  • Develop innovative data and hybrid drive modelling algorithms to increase the accuracy of machinery and factory models, discover new knowledge and to allow tuning of physical models;
  • Develop cross-context models to allow machine learning models to adapt to new contexts wherin machines and factories need to operate;
  • Introduce virtual, augmented and mixed reality into the manufacutring industry based on/inspired by the gaming industry.

Topics

Blue collar training based on AR/VR

DAE.pngUsing the Virtual Reality training application made by howest DAE-Research, Flemish Minister Crevits learned how to manually assemble a machine part step by step. Embedding the graduation work of student Giuliano De Luca made it possible to blend the virtual and real world by using the greenkey studio inside the Level, thus creating a mixed reality experience. The tools are being developed a.o. within the VLAIO-TETRA project 'Sector Innovating Virtual & Augmented Reality'. In this project Flemish organisations are guided into defining the added value of Virtual & Augmented Reality technology in their current workflow. One part of the project focusses on developing proof-of-concept applications for Flemish industrial settings (e.g. remote support, virtual reality training,...) with the aid of Howest, application and game developers.

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Sensor Fusion & Dynamic Dashboards

DynamicDashboard.jpgBy designing multi-modal and multi-sensor architectures we provide collaboration among sensors (classical, video-based, time-series, mobile sensing and virtual/data mining sensors) in order to feed back the available information and intelligence of all sensors to optimize their functionality and enhance the detection and interpretation of advanced events. A wide range of applications can benefit from these multi-modal and multi-sensor architectures that fuse amongst other visual, audio, thermal, vibration and/or data mining information. Examples are industrial process control or condition monitoring. 

The heterogeneity and vast amount of sensors, as well as the difficulty of creating interesting sensor data combinations, also hinder the deployment of fixed structure dashboards as they are unable to cope with the accordingly vast amount of required mappings. Therefore, we additionally develop dynamic dashboards that precisely visualize the interesting data for the end-user produced by sensors in multi-sensor environments by dynamically generating meaningful service compositions, allowing the detection of complex events that used to remain undetected.

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Research & test infrastructure

See here our list of infrastructure