CREAM, a component level coffeemaker electrical activity measurement dataset

Daniel Jorde, Thomas Kriechbaumer, Tim Berger, Stefan Zitzlsperger, Hans Arno Jacobsen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Monitoring the internal conditions of a machine is essential to increase its production efficiency and to reduce energy waste. Non-intrusive condition monitoring techniques, such as analysing electrical signals, provide insights by disaggregating a composite signal of a machine as a whole into the individual components to determine their states. Developing and evaluating new algorithms for condition monitoring and maintenance-related analysis tasks require a fully-labelled dataset for a machine, which comprises standard industrial components that are triggered following a typical manufacturing process to produce goods. For this purpose, we introduce CREAM, a component level electrical measurement dataset for two industrial-grade coffeemakers, simulating industrial processes. The dataset contains continuous voltage and current measurements provided at 6400 samples per second, as well as the product and maintenance-related event labels, such as 370600 expert-labelled component-level electrical events, 1734 product ones and 3646 maintenance ones. CREAM provides fully-labelled ground-truth to establish a benchmark and comparative studies of manufacturing-related analysis in a controlled and transparent environment.

Original languageEnglish
Article number441
JournalScientific Data
Issue number1
StatePublished - Dec 2020


Dive into the research topics of 'CREAM, a component level coffeemaker electrical activity measurement dataset'. Together they form a unique fingerprint.

Cite this