3 citations · 8 across the 7 of their papers we have counts for
6 papers · 1 filter
Federated Learning for Anomaly Detection in Maritime Movement Data
Anita Graser, Axel Weißenfeld, Clemens Heistracher +2
This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce comm…
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
Viktorija Pruckovskaja, Axel Weissenfeld, Clemens Heistracher +5
Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learn…
Smart Active Sampling to enhance Quality Assurance Efficiency
Clemens Heistracher, Stefan Stricker, Pedro Casas +2
We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line. Based on the principles of active learning a machine learning…
Machine Learning Methods for Health-Index Prediction in Coating Chambers
Clemens Heistracher, Anahid Jalali, Jürgen Schneeweiss +3
Coating chambers create thin layers that improve the mechanical and optical surface properties in jewelry production using physical vapor deposition. In such a process, evaporated…
Minimal-Configuration Anomaly Detection for IIoT Sensors
Clemens Heistracher, Anahid Jalali, Axel Suendermann +4
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration ef…
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Anahid Jalali, Clemens Heistracher, Alexander Schindler +5
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex…