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Found 17 papers, 2 papers with code
LLM experiments with simulation: Large Language Model Multi-Agent System for Process Simulation Parametrization in Digital Twins
This paper presents a novel design of a multi-agent system framework that applies a large language model (LLM) to automate the parametrization of process simulations in digital twins.
Generation of Asset Administration Shell with Large Language Model Agents: Towards Semantic Interoperability in Digital Twins in the Context of Industry 4.0
Then, a system powered by large language models is designed and implemented to process the "semantic node" and generate standardized digital twin models from raw textual data collected from datasheets describing technical assets.
A Modular System Architecture for an Offshore Off-grid Platform for Climate neutral Power-to-X Production in H2Mare
Power-to-X (PtX) products constitute a promising solution component in the defossilisation of hard-to-abate sectors.
Towards autonomous system: flexible modular production system enhanced with large language model agents
In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes.
Automated data-driven creation of the Digital Twin of a brownfield plant
The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system.
Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management
Many decision-making approaches rely on the exploration of solution spaces with regards to specified criteria.
Stuttgart Open Relay Degradation Dataset (SOReDD)
However, open-source datasets suitable for transfer learning training, i. e. spanning different assets, processes and data (variants), are rare.
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts.
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks
In this paper, a methodology is presented that creates worstcase images using image augmentation techniques.
Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries
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.
Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models
Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future.
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data
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
Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems.
A Survey on Anomaly Detection for Technical Systems using LSTM Networks
Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure.
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing
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.
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
In this article, the concepts of transfer and continual learning are introduced.
Transfer Learning as an Enabler of the Intelligent Digital Twin
Looking at common challenges in developing and deploying industrial machinery with deep learning functionalities, embracing this concept would offer several advantages: Using an intelligent Digital Twin, learning algorithms can be designed, configured and tested in the design phase before the physical system exists and real data can be collected.
