---
_id: '30865'
abstract:
- lang: eng
text: "The problem of selecting an algorithm that appears most suitable for a\r\nspecific
instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability
problem, is called instance-specific algorithm selection. Over\r\nthe past decade,
the problem has received considerable attention, resulting in\r\na number of different
methods for algorithm selection. Although most of these\r\nmethods are based on
machine learning, surprisingly little work has been done\r\non meta learning,
that is, on taking advantage of the complementarity of\r\nexisting algorithm selection
methods in order to combine them into a single\r\nsuperior algorithm selector.
In this paper, we introduce the problem of meta\r\nalgorithm selection, which
essentially asks for the best way to combine a given\r\nset of algorithm selectors.
We present a general methodological framework for\r\nmeta algorithm selection
as well as several concrete learning methods as\r\ninstantiations of this framework,
essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive
experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors
can significantly outperform single\r\nalgorithm selectors and have the potential
to form the new state of the art in\r\nalgorithm selection."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Lukas
full_name: Gehring, Lukas
last_name: Gehring
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection
on a Meta Level. Machine Learning. Published online 2022.
apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E.
(2022). Algorithm Selection on a Meta Level. In Machine Learning.
bibtex: '@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm
Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander
and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2022} }'
chicago: Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever,
and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning,
2022.
ieee: A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm
Selection on a Meta Level,” Machine Learning. 2022.
mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine
Learning, 2022.
short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning
(2022).
date_created: 2022-04-12T11:55:18Z
date_updated: 2022-08-24T12:45:39Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2107.09414'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Machine Learning
status: public
title: Algorithm Selection on a Meta Level
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '36227'
author:
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Volker
full_name: Lohweg, Volker
last_name: Lohweg
- first_name: Alexander
full_name: Schneider, Alexander
last_name: Schneider
- first_name: Wolfram
full_name: Schenck, Wolfram
last_name: Schenck
- first_name: Ulrike
full_name: Kuhl, Ulrike
last_name: Kuhl
- first_name: Marco
full_name: Braun, Marco
last_name: Braun
- first_name: Anton
full_name: Pfeifer, Anton
last_name: Pfeifer
- first_name: Christoph-Alexander
full_name: Holst, Christoph-Alexander
last_name: Holst
- first_name: Malte
full_name: Schmidt, Malte
last_name: Schmidt
- first_name: Gunnar
full_name: Schomaker, Gunnar
last_name: Schomaker
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
citation:
ama: 'Hammer B, Hüllermeier E, Lohweg V, et al. Schlussbericht ITS.ML: Intelligente
Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens.; 2022.
doi:10.4119/unibi/2965622'
apa: 'Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl,
U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede,
T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten
Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse
von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622'
bibtex: '@book{Hammer_Hüllermeier_Lohweg_Schneider_Schenck_Kuhl_Braun_Pfeifer_Holst_Schmidt_et
al._2022, title={Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten
Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse
von Daten mittels Maschinellen Lernens}, DOI={10.4119/unibi/2965622},
author={Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider,
Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer,
Anton and Holst, Christoph-Alexander and Schmidt, Malte and et al.}, year={2022}
}'
chicago: 'Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider,
Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. Schlussbericht ITS.ML: Intelligente
Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022.
https://doi.org/10.4119/unibi/2965622.'
ieee: 'B. Hammer et al., Schlussbericht ITS.ML: Intelligente Technische
Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022.'
mla: 'Hammer, Barbara, et al. Schlussbericht ITS.ML: Intelligente Technische
Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022,
doi:10.4119/unibi/2965622.'
short: 'B. Hammer, E. Hüllermeier, V. Lohweg, A. Schneider, W. Schenck, U. Kuhl,
M. Braun, A. Pfeifer, C.-A. Holst, M. Schmidt, G. Schomaker, T. Tornede, Schlussbericht
ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles
Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen
Lernens, 2022.'
date_created: 2023-01-11T15:00:00Z
date_updated: 2023-01-11T15:20:40Z
ddc:
- '004'
department:
- _id: '34'
- _id: '7'
- _id: '534'
doi: 10.4119/unibi/2965622
has_accepted_license: '1'
language:
- iso: ger
status: public
title: 'Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation
durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten
mittels Maschinellen Lernens'
type: report
user_id: '40795'
year: '2022'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '30866'
abstract:
- lang: eng
text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof
machine learning algorithms and their composition into an overall (software)\r\nsolution
- a machine learning pipeline - tailored to the learning task\r\n(dataset) at
hand. Over the last decade, AutoML has developed into an\r\nindependent research
field with hundreds of contributions. While AutoML offers\r\nmany prospects, it
is also known to be quite resource-intensive, which is one\r\nof its major points
of criticism. The primary cause for a high resource\r\nconsumption is that many
approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines
while searching for good candidates. This problem is\r\namplified in the context
of research on AutoML methods, due to large scale\r\nexperiments conducted with
many datasets and approaches, each of them being run\r\nwith several repetitions
to rule out random effects. In the spirit of recent\r\nwork on Green AI, this
paper is written in an attempt to raise the awareness of\r\nAutoML researchers
for the problem and to elaborate on possible remedies. To\r\nthis end, we identify
four categories of actions the community may take towards\r\nmore sustainable
research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking,
transparency and research incentives."
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards
Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850.
Published online 2021.'
apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier,
E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
In arXiv:2111.05850.'
bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards
Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850},
author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik
Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning:
Status Quo and Future Directions.” ArXiv:2111.05850, 2021.'
ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier,
“Towards Green Automated Machine Learning: Status Quo and Future Directions,”
arXiv:2111.05850. 2021.'
mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo
and Future Directions.” ArXiv:2111.05850, 2021.'
short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier,
ArXiv:2111.05850 (2021).
date_created: 2022-04-12T11:57:15Z
date_updated: 2022-04-12T12:01:23Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2111.05850'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2111.05850
status: public
title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions'
type: preprint
user_id: '38209'
year: '2021'
...
---
_id: '17424'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive
Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020.
; 2020. doi:10.1007/978-3-030-66770-2_8'
apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020).
AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of
the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8'
bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML
for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8},
booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede,
Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020}
}'
chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and
Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”
In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.'
ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML
for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream
Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.'
mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL
Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.'
short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings
of the ECMLPKDD 2020, 2020.'
conference:
name: IOTStream Workshop @ ECMLPKDD 2020
date_created: 2020-07-28T09:17:41Z
date_updated: 2022-01-06T06:53:11Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1007/978-3-030-66770-2_8
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '1'
name: SFB 901
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the ECMLPKDD 2020
status: public
title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17426'
abstract:
- lang: eng
text: The development of renewable energies and smart mobility has profoundly
impacted the future of the distribution grid. An increasing bidirectional energy
flow stresses the assets of the distribution grid, especially medium voltage switchgear.
This calls for improved maintenance strategies to prevent critical failures. Predictive
maintenance, a maintenance strategy relying on current condition data of assets,
serves as a guideline. Novel sensors covering thermal, mechanical, and partial
discharge aspects of switchgear, enable continuous condition monitoring of some
of the most critical assets of the distribution grid. Combined with machine learning
algorithms, the demands put on the distribution grid by the energy and mobility
revolutions can be handled. In this paper, we review the current state-of-the-art
of all aspects of condition monitoring for medium voltage switchgear. Furthermore,
we present an approach to develop a predictive maintenance system based on novel
sensors and machine learning. We show how the existing medium voltage grid infrastructure
can adapt these new needs on an economic scale.
article_number: '2099'
author:
- first_name: Martin W.
full_name: Hoffmann, Martin W.
last_name: Hoffmann
- first_name: Stephan
full_name: Wildermuth, Stephan
last_name: Wildermuth
- first_name: Ralf
full_name: Gitzel, Ralf
last_name: Gitzel
- first_name: Aydin
full_name: Boyaci, Aydin
last_name: Boyaci
- first_name: Jörg
full_name: Gebhardt, Jörg
last_name: Gebhardt
- first_name: Holger
full_name: Kaul, Holger
last_name: Kaul
- first_name: Ido
full_name: Amihai, Ido
last_name: Amihai
- first_name: Bodo
full_name: Forg, Bodo
last_name: Forg
- first_name: Michael
full_name: Suriyah, Michael
last_name: Suriyah
- first_name: Thomas
full_name: Leibfried, Thomas
last_name: Leibfried
- first_name: Volker
full_name: Stich, Volker
last_name: Stich
- first_name: Jan
full_name: Hicking, Jan
last_name: Hicking
- first_name: Martin
full_name: Bremer, Martin
last_name: Bremer
- first_name: Lars
full_name: Kaminski, Lars
last_name: Kaminski
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
citation:
ama: Hoffmann MW, Wildermuth S, Gitzel R, et al. Integration of Novel Sensors and
Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable
the Energy and Mobility Revolutions. Sensors. 2020. doi:10.3390/s20072099
apa: Hoffmann, M. W., Wildermuth, S., Gitzel, R., Boyaci, A., Gebhardt, J., Kaul,
H., … Tornede, T. (2020). Integration of Novel Sensors and Machine Learning for
Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility
Revolutions. Sensors. https://doi.org/10.3390/s20072099
bibtex: '@article{Hoffmann_Wildermuth_Gitzel_Boyaci_Gebhardt_Kaul_Amihai_Forg_Suriyah_Leibfried_et
al._2020, title={Integration of Novel Sensors and Machine Learning for Predictive
Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions},
DOI={10.3390/s20072099}, number={2099},
journal={Sensors}, author={Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel,
Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and
Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and et al.}, year={2020}
}'
chicago: Hoffmann, Martin W., Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Jörg
Gebhardt, Holger Kaul, Ido Amihai, et al. “Integration of Novel Sensors and Machine
Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the
Energy and Mobility Revolutions.” Sensors, 2020. https://doi.org/10.3390/s20072099.
ieee: M. W. Hoffmann et al., “Integration of Novel Sensors and Machine Learning
for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and
Mobility Revolutions,” Sensors, 2020.
mla: Hoffmann, Martin W., et al. “Integration of Novel Sensors and Machine Learning
for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and
Mobility Revolutions.” Sensors, 2099, 2020, doi:10.3390/s20072099.
short: M.W. Hoffmann, S. Wildermuth, R. Gitzel, A. Boyaci, J. Gebhardt, H. Kaul,
I. Amihai, B. Forg, M. Suriyah, T. Leibfried, V. Stich, J. Hicking, M. Bremer,
L. Kaminski, D. Beverungen, P. zur Heiden, T. Tornede, Sensors (2020).
date_created: 2020-07-28T09:47:36Z
date_updated: 2022-01-06T06:53:11Z
doi: 10.3390/s20072099
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/1424-8220/20/7/2099
oa: '1'
publication: Sensors
publication_identifier:
issn:
- 1424-8220
publication_status: published
status: public
title: Integration of Novel Sensors and Machine Learning for Predictive Maintenance
in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions
type: journal_article
user_id: '40795'
year: '2020'
...