most citedA Survey on Anomaly Detection for Technical Systems using LSTM Networks

442 citations · 445 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG20211 cited

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…

cs.SE20211 cited

Towards establishing formal verification and inductive code synthesis in the PLC domain

Matthias Weiß, Philipp Marks, Benjamin Maschler +3

Nowadays, formal methods are used in various areas for the verification of programs or for code generation from models in order to increase the quality of software and to reduce co…

cs.LG2021

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.…

cs.LG20211 cited

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…

cs.LG2021442 cited

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…

cs.LG2021

Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

Benjamin Maschler, Thi Thu Huong Pham, Michael Weyrich

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While…