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CREAM, a component level coffeemaker electrical activity measurement dataset.

Daniel Jorde1, Thomas Kriechbaumer2, Tim Berger2

  • 1Department of Computer Science, Chair for Application and Middleware Systems, Technical University of Munich, 85748, Garching, Germany. daniel.jorde@tum.de.

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This summary is machine-generated.

This study introduces CREAM, a novel dataset for machine condition monitoring. It provides labeled electrical data from coffeemakers to benchmark new algorithms for improved efficiency and reduced energy waste.

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Area of Science:

  • Industrial Engineering
  • Electrical Engineering
  • Data Science

Background:

  • Machine condition monitoring is crucial for production efficiency and energy conservation.
  • Non-intrusive techniques analyzing electrical signals enable component-level state determination.
  • Developing new algorithms requires fully-labeled datasets of industrial processes.

Purpose of the Study:

  • Introduce CREAM, a component-level electrical measurement dataset.
  • Provide a benchmark for evaluating condition monitoring and maintenance algorithms.
  • Simulate industrial processes using standard components.

Main Methods:

  • Collected continuous voltage and current measurements at 6400 samples/sec from two industrial coffeemakers.
  • Developed expert-labeled datasets for component-level electrical events, product events, and maintenance events.
  • Utilized coffeemakers to simulate typical manufacturing processes.

Main Results:

  • The CREAM dataset contains 370,600 labeled electrical events, 1,734 product events, and 3,646 maintenance events.
  • Provides fully-labeled ground-truth for component-level analysis.
  • Establishes a benchmark for comparative studies in a controlled environment.

Conclusions:

  • CREAM facilitates the development and evaluation of advanced condition monitoring algorithms.
  • The dataset supports research in manufacturing process analysis and predictive maintenance.
  • Enables transparent and reproducible studies in industrial settings.