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  4. Data-based condition monitoring and disturbance classification in actively controlled laser oscillators
 
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Data-based condition monitoring and disturbance classification in actively controlled laser oscillators

Citation Link: https://doi.org/10.15480/882.14193
Publikationstyp
Conference Paper
Date Issued
2024
Sprache
English
Author(s)
Grünhagen, Arne  
Eichler, Annika  
Regelungstechnik E-14  
Tropmann-Frick, Marina  
Fey, Görschwin  orcid-logo
Eingebettete Systeme E-13  
TORE-DOI
10.15480/882.14193
TORE-URI
https://tore.tuhh.de/handle/11420/52855
First published in
Frontiers in artificial intelligence and applications  
Number in series
380
Start Page
94
End Page
114
Citation
33rd International Conference on Information Modeling and Knowledge Bases, EJC 2023
Contribution to Conference
33rd International Conference on Information Modeling and Knowledge Bases, EJC 2023  
Publisher DOI
10.3233/faia231149
Publisher
IOS Press
ISBN
978-1-64368-477-2
978-1-64368-476-5
Peer Reviewed
true
The successful operation of the laser-based synchronization system of the European X-Ray Free Electron Laser relies on the precise functionality of numerous dynamic systems operating within closed loops with controllers. In this paper, we present how data-based machine learning methods can detect and classify disturbances to such dynamic systems based on the controller output signal. We present 4 feature extraction methods based on statistics in the time domain, statistics in the frequency domain, characteristics of spectral peaks, and the autoencoder latent space representation of the frequency domain. These feature extraction methods require no system knowledge and can easily be transferred to other dynamic systems. We combine feature extraction, fault detection, and fault classification into a comprehensive and fully automated condition monitoring pipeline. For that, we systematically compare the performance of 19 state-of-the-art fault detection and 4 classification algorithms to decide which combination of feature extraction and fault detection or classification algorithm is most appropriate to model the condition of an actively controlled phase-locked laser oscillator. Our experimental evaluation shows the effectiveness of clustering algorithms, showcasing their strong suitability in detecting perturbed system conditions. Furthermore, in our evaluation, the support vector machine proves to be the most suitable for classifying the various disturbances.
Subjects
Fault detection
Fault classification
Feature Extraction
Autoencoder
MLE@TUHH
DDC Class
004: Computer Sciences
Lizenz
https://creativecommons.org/licenses/by/4.0/
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