Title: Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems
Authors: Voß, Carlo
Eiteneuer, Benedikt
Niggemann, Oliver 
Language: en
Keywords: Universitätsbibliographie;Evaluation 2020
Issue Date: 10-Jun-2020
Publisher: IEEE
Document Type: Conference Object
Source: Enthalten in: 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS). - Piscataway, NJ : IEEE, 2020. - 1 Online-Ressource . - 2020, Seite 475-480
Page Start: 475
Page End: 480
Pages: 475 - 480
Publisher Place: Piscataway, NJ
Conference: 3rd IEEE International Conference on Industrial Cyber-Physical Systems 
Abstract: 
© 2020 IEEE. In the field of Cyber-Physical Systems (CPS), the early detection of anomalies is crucial to avoid future faulty behaviors, e.g. preventing downtimes or decreasing product qualities. As a solution, unsupervised machine learning can be used to learn models of the historic system behavior and consequentially detect deviations from these models. Since CPS data are high dimensional time series, suitable approaches such as Long Short-Term Memory (LSTM) neural networks are good solution candidates.CPSs have specific requirements for such machine learning algorithms. Learned models must be especially useful in closed control loops, i.e. without human supervision. For this, it is essential that the uncertainty about model predictions is also part of the learned models. In order to incorporate such uncertainties, we modify the mean squared error loss function used by LSTM. This paper also analyses the solution on artificial and real data.
Organization Units (connected with the publication): Informatik im Maschinenbau 
URL: https://ub.hsu-hh.de/DB=1.8/XMLPRS=N/PPN?PPN=1756961948
https://www.researchgate.net/publication/340502169
https://api.elsevier.com/content/abstract/scopus_id/85098693473
DOI: 10.1109/ICPS48405.2020.9274779
Appears in Collections:2020

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