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Energies 2020, 13(2), 399; https://doi.org/10.3390/en13020399

Addendum
Addendum: Termite, M.R. et al. A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments. Energies 2019, 12, 4802
1
Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
2
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
3
Aramis Srl, Via pergolesi 5, 20121 Milano, Italy
4
MINES ParisTech, PSL Research University, CRC, 06560 Sophia Antipolis, France
5
Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Seoul 130-701, Korea
*
Author to whom correspondence should be addressed.
Received: 27 December 2019 / Accepted: 8 January 2020 / Published: 13 January 2020
The authors would like to add the following note to Figure 3 of their paper published in Energies [1]:
The image used to represent the clustering module in Figure 3 was taken from Figure 6 of [2], reverted and without the text labels of the original figure.
The manuscript will be updated, and the original one will remain available on the article webpage, with reference to this Addendum. The authors apologize for any inconvenience this change may cause. The changes do not affect the scientific results.

References

  1. Termite, M.R.; Baraldi, P.; Al-Dahidi, S.; Bellani, L.; Compare, M.; Zio, E. A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments. Energies 2019, 12, 4802. [Google Scholar] [CrossRef]
  2. Hao, Y.; Chen, Y.; Zakaria, J.; Hu, B.; Rakthanmanon, T.; Keogh, E. Towards Never-Ending Learning from Time Series Streams. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 11–14 August 2013; ACM: New York, NY, USA, 2013; pp. 874–882. [Google Scholar]
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