442 citations · 902 across the 47 of their papers we have counts for
8 papers · 1 filter
An Agent-based Realisation for a continuous Model Adaption Approach in intelligent Digital Twins
Daniel Dittler, Peter Lierhammer, Dominik Braun +3
The trend in industrial automation is towards networking, intelligence and autonomy. Digital Twins, which serve as virtual representations, are becoming increasingly important in t…
A graph-based knowledge representation and pattern mining supporting the Digital Twin creation of existing manufacturing systems
Dominik Braun, Timo Müller, Nada Sahlab +3
The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of bro…
Automated data-driven creation of the Digital Twin of a brownfield plant
Dominik Braun, Wolfgang Schloegl, Michael Weyrich
The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned…
An Overview on Designs and Applications of Context-Aware Automation Systems
Nada Sahlab, Nasser Jazdi, Michael Weyrich
Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic ch…
A Knowledge Graph-Based Method for Automating Systematic Literature Reviews
Nada Sahlab, Hesham Kahoul, Nasser Jazdi +1
Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research…
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…