Big data is efficient only in the right context
Networked machines produce a continuous stream of data, but measurement data alone does not offer companies any added value: What is needed is an interface to correlate data from various sources.10 Feb 2019 Kai Tubbesing
The research project BigPro , carried out at the Institute for Industrial Management FIR at RWTH Aachen University between 2014 and 2017 and funded by the German Federal Ministry of Education and Research (BMBF), investigated the potential of big data technologies, with the focus on the question of how automated data streams and their evaluation can best be leveraged to support fault management in production. The key component here was a data model, developed by Karlsruhe-based ERP specialists Asseco Solutions, for embedding measurements in specific context information.
The researchers tested their solution under real conditions at the project partners Bosch and Auto Heinen , where data streams were collected from various sources and interlinked, whereby the predictive management solution was able to collect the data in real time and put it in context with metainformation, such as the relevant machine location, last maintenance date, similar faults in the past, and how long it took to troubleshoot those faults. Based on this information, the system proposed prioritized solutions for the early prevention of possible machine failure.
The project participants are not the only ones to pursue this basic objective: Numerous other research projects are also currently looking at how big data and predictive maintenance can make industrial production more efficient.
Interested in news about exhibitors, top offers and trends in the industry?
Your web browser is outdated. Update your browser for more security, speed and optimal presentation of this page.Update Browser