442 citations · 446 across the 7 of their papers we have counts for
9 papers · 1 filter
Stuttgart Open Relay Degradation Dataset (SOReDD)
Benjamin Maschler, Angel Iliev, Thi Thu Huong Pham +1
Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning…
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
Benjamin Maschler, Tim Knodel, Michael Weyrich
Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropria…
Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries
Benjamin Maschler, Sophia Tatiyosyan, Michael Weyrich
In recent years, the use of lithium-ion batteries has greatly expanded into products from many industrial sectors, e.g. cars, power tools or medical devices. An early prediction an…
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data
Benjamin Maschler, Tim Knodel, Michael Weyrich
Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks.…
Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems
Benjamin Maschler, Timo Müller, Andreas Löcklin +1
Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from…
A Survey on Anomaly Detection for Technical Systems using LSTM Networks
Benjamin Lindemann, Benjamin Maschler, Nada Sahlab +1
Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are…