442 citations · 636 across the 11 of their papers we have counts for
12 papers
A Survey on Deep Industrial Transfer Learning in Fault Prognostics
Benjamin Maschler
Due to its probabilistic nature, fault prognostics is a prime example of a use case for deep learning utilizing big data. However, the low availability of such data sets combined w…
Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management
Timo Müller, Benjamin Maschler, Daniel Dittler +2
Many decision-making approaches rely on the exploration of solution spaces with regards to specified criteria. However, in complex environments, brute-force exploration strategies…
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 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…
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…
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…
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
Benjamin Maschler, Michael Weyrich
In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing me…
Transfer Learning as an Enabler of the Intelligent Digital Twin
Benjamin Maschler, Dominik Braun, Nasser Jazdi +1
Digital Twins have been described as beneficial in many areas, such as virtual commissioning, fault prediction or reconfiguration planning. Equipping Digital Twins with artificial…