1 citations · 1 across the 5 of their papers we have counts for
6 papers
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information
Henrik Sebastian Steude, Alexander Diedrich, Ingo Pill +3
Diagnostic processes for complex cyber-physical systems often require extensive prior knowledge in the form of detailed system models or comprehensive training data. However, obtai…
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
Alexander Windmann, Henrik Steude, Daniel Boschmann +1
Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While D…
Diagnosis driven Anomaly Detection for CPS
Henrik S. Steude, Lukas Moddemann, Alexander Diedrich +2
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isola…
Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Lukas Moddemann, Henrik Sebastian Steude, Alexander Diedrich +1
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time se…
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Alexander Windmann, Henrik Steude, Oliver Niggemann
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of th…
Learning Physical Concepts in Cyber-Physical Systems: A Case Study
Henrik S. Steude, Alexander Windmann, Oliver Niggemann
Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize syst…