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Digital moka: Small-scale condition monitoring in process engineering

Abstract

In this letter, we present a data-driven condition-monitoring system for a moka pot aiming at anomaly detection in the coffee-preparation process. A data-acquisition system and the corresponding generation process of a comprehensive dataset (including data from ideal and anomalous brewing scenarios) are described. Supervised and unsupervised machine learning algorithms are trained and tested on the dataset aiming at detecting anomalies in the process and showing the relevance of the considered framework.

Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Energy Research / Gassteknologi
  • University of Naples 'Federico II'
  • Norwegian University of Science and Technology
  • Texas Instruments Norway

Year

2021

Published in

IEEE Sensors Letters

Volume

5

Issue

3

View this publication at Norwegian Research Information Repository