Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems Article Swipe
Karl-Philipp Kortmann
,
Moritz Fehsenfeld
,
Mark Wielitzka
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.02784
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.02784
Sensor and control data of modern mechatronic systems are often available as heterogeneous time series with different sampling rates and value ranges. Suitable classification and regression methods from the field of supervised machine learning already exist for predictive tasks, for example in the context of condition monitoring, but their performance scales strongly with the number of labeled training data. Their provision is often associated with high effort in the form of person-hours or additional sensors. In this paper, we present a method for unsupervised feature extraction using autoencoder networks that specifically addresses the heterogeneous nature of the database and reduces the amount of labeled training data required compared to existing methods. Three public datasets of mechatronic systems from different application domains are used to validate the results.
Related Topics
Concepts
Autoencoder
Computer science
Artificial intelligence
Context (archaeology)
Machine learning
Multivariate statistics
Mechatronics
Field (mathematics)
Unsupervised learning
Representation (politics)
Time series
Supervised learning
Data mining
Feature (linguistics)
Ranking (information retrieval)
Feature learning
Artificial neural network
Mathematics
Politics
Biology
Linguistics
Paleontology
Law
Political science
Philosophy
Pure mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2104.02784.pdf
- OA Status
- green
- Cited By
- 1
- References
- 15
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3150193503
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3150193503Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.02784Digital Object Identifier
- Title
-
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-06Full publication date if available
- Authors
-
Karl-Philipp Kortmann, Moritz Fehsenfeld, Mark WielitzkaList of authors in order
- Landing page
-
https://arxiv.org/pdf/2104.02784.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2104.02784.pdfDirect OA link when available
- Concepts
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Autoencoder, Computer science, Artificial intelligence, Context (archaeology), Machine learning, Multivariate statistics, Mechatronics, Field (mathematics), Unsupervised learning, Representation (politics), Time series, Supervised learning, Data mining, Feature (linguistics), Ranking (information retrieval), Feature learning, Artificial neural network, Mathematics, Politics, Biology, Linguistics, Paleontology, Law, Political science, Philosophy, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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-
20Other works algorithmically related by OpenAlex
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| publication_date | 2021-04-06 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2920777619, https://openalex.org/W2963919714, https://openalex.org/W3103543904, https://openalex.org/W3114313061, https://openalex.org/W1582873796, https://openalex.org/W2317595875, https://openalex.org/W2911200746, https://openalex.org/W2975649990, https://openalex.org/W2962897886, https://openalex.org/W51642679, https://openalex.org/W2933697479, https://openalex.org/W3010542492, https://openalex.org/W2951004968, https://openalex.org/W2963912395, https://openalex.org/W2964121744 |
| referenced_works_count | 15 |
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