Challenges for Data Mining on Sensor Data of Interlinked Processes
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Date
2012-02-28
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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.