6 papers
Industrial AI Robustness Card for Time Series Models
Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko +1
Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the In…
Benchmarking Sensor-Fault Robustness in Forecasting
Alexander Windmann, Philipp Wittenberg, Gianluca Manca +3
Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standard forecasting evaluation often se…
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…
MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection
Joshua Schraven, Alexander Windmann, Oliver Niggemann
Benchmark datasets for network intrusion detection commonly rely on synthetically generated traffic, which fails to reflect the statistical variability and temporal drift encounter…
Evaluating Large Language Models for Real-World Engineering Tasks
Rene Heesch, Sebastian Eilermann, Alexander Windmann +3
Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critic…
Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
Alexander Windmann, Philipp Wittenberg, Marvin Schieseck +1
In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and pr…