Hybrid online timed automaton learning algorithm for discrete manufacturing systems

Publication date
2024-03
Document type
Conference paper
Author
Martens, Simon
Maier, Alexander
Wunn, Alexander
Organisational unit
Haver & Boecker OHG
Bielefeld University of Applied Sciences
DOI
URI
Conference
Book title
Machine learning for cyber physical systems
Is part of
Part of the university bibliography
✅
Files
Keyword
Cyber physical production system
Model identification
Hybrid timed automata
Online
Unsupervised
Anomaly detection
Abstract
This paper presents the Hybrid Online Timed Automaton Learning Algorithm (HyOTALA), a novel approach for anomaly detection in cyber-physical production systems (CPPS). It addresses the challenge of using hybrid data, combining sparse discrete and continuous signals, by learning a hybrid timed automaton model in an online setting. This model captures the dynamics of discrete manufacturing processes and provides significant advances in model identification for CPPS. The effectiveness of HyOTALA is demonstrated through a practical application, highlighting its potential to improve anomaly detection capabilities in industrial settings.
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