Authors: Deuse, Jochen
Konrad, Benedikt
Lieber, Daniel
Morik, Katharina
Stolpe, Marco
Title: Challenges for Data Mining on Sensor Data of Interlinked Processes
Language (ISO): en
Abstract: In industries like steel production, interlinked production processes leave no time for assessing the physical quality of intermediate products. Failures during the process can lead to high internal costs when already defective products are passed through the entire value chain. However, process data like machine parameters and sensor data which are di- rectly linked to quality can be recorded. Based on a rolling mill case study, the paper discusses how decentralized data mining and intelligent machine-to-machine communication could be used to predict the physical quality of intermediate products online and in real-time for detecting quality issues as early as possible. The recording of huge data masses and the distributed but sequential nature of the problem lead to challenging research questions for the next generation of data mining.
URI: http://hdl.handle.net/2003/29341
http://dx.doi.org/10.17877/DE290R-3319
Issue Date: 2012-02-28
Is part of: Proc. of the Next Generation Data Mining Summit 2011: Ubiquitous Knowledge Discovery for Energy Management in Smart Grids and Intelligent Machine-to-Machine (M2M) Telematics
Appears in Collections:Sonderforschungsbereich (SFB) 876

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