2 citations · 4 across the 8 of their papers we have counts for
9 papers
Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets
Philipp Sepin, Jana Kemnitz, Safoura Rezapour Lakani +1
Vibration-based condition monitoring systems are receiving increasing attention due to their ability to accurately identify different conditions by capturing dynamic features over…
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…
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems
Stephanie Holly, Robin Heel, Denis Katic +8
Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the…
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…
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System
Stephanie Holly, Thomas Hiessl, Safoura Rezapour Lakani +3
Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying le…
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…