Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 7 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 8 Apr. 2021 |
Abstract
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2021.
Publikation: Arbeitspapier/Preprint › Preprint
}
TY - UNPB
T1 - Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems
AU - Kortmann, Karl-Philipp
AU - Fehsenfeld, Moritz
AU - Wielitzka, Mark
N1 - A later version of this paper in German language was submitted to VDI Mechatronic Tagung 2021 and will be published in the conference proceedings
PY - 2021/4/8
Y1 - 2021/4/8
N2 - 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.
AB - 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.
KW - cs.LG
KW - cs.AI
KW - eess.SP
KW - 68T05 (Primary) 62H12, 68T07 (Secondary)
KW - J.2
M3 - Preprint
BT - Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems
ER -