Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic Systems

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 c...

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Bibliographic Details
Published inarXiv.org
Main Authors Karl-Philipp Kortmann, Fehsenfeld, Moritz, Wielitzka, Mark
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.04.2021
Subjects
Condition monitoring
Control data (computers)
Feature extraction
Machine learning
Multivariate analysis
Time series
Training
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Summary: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.
ISSN:2331-8422