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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Liu, Gaoxiang Liu, Kexin Chen, Lei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819733354

In this paper, we discuss the collective behavior of drone swarms and remark that the swarm behavior can be realized by pinning a few nodes. Additionally, the selection of pinning nodes effects the global characteristic. Compared with other methods, the uniform degree selection of pinning nodes in a swarm exhibits better performance. In order to verify the effectiveness of our pinning strategy, we conduct simulations in a self-driven drone swarm, in which the experimental results confirm the availability of our conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drones

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REINFORCEMENT LEARNING AND GRAPH NEURAL NETWORKS FOR PROBABILISTIC RISK ASSESSMENT

arXiv

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arXiv 2024年

作者： Grimstad, Joachim Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNN). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper’s original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can optimize and partially substitute classical PRA solvers that are based on existing formal methods. Such an approach helps to solve the problem of the fast-growing complexity of PRA models of modern technical systems. © 2024, CC BY-SA.

关键词： Reinforcement learning

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Towards autonomous system: flexible modular production system enhanced with large language model agents

Towards autonomous system: flexible modular production syste...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Yuchen Xia Manthan Shenoy Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

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. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://***/YuchenXia/GPT4Industrialautomation

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Synthetic Data Generation for improving Deep Learning-based 5G Indoor Positioning

Synthetic Data Generation for improving Deep Learning-based ...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Hannes Vietz Manuel Hirth Sebastian Baum Michael Weyrich Institute of Industrial Automation and Software Engineering University Stuttgart Stuttgart Germany

Gathering sufficient labeled training data to effectively train a high-performing deep learning model can be particularly challenging in the realm of industrial automation. Depending on the data type, this may require expensive interruptions to production processes or similar disruptions on the factory floor for data collection. It is often uncertain which data types are crucial for enhancing the performance of the trained model. For vision models, factors such as specific viewing angles or lighting conditions may be important, while for models utilizing radio signals, unique reflections generated by moving metal surfaces could be significant. Moreover, data labeling is expensive as it is primarily conducted manually by human workers. This paper demonstrates how to automatically generate relevant, labeled synthetic training data to boost a neural network's accuracy for deep learning-based 5G indoor positioning tasks. We reveal that employing this generated synthetic data to train a convolutional neural network can improve its median positioning accuracy by a notable 25%.

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Automated and Continuous Risk Assessment for ROS-Based software-Defined Robotic Systems

Automated and Continuous Risk Assessment for ROS-Based Softw...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Philipp Grimmeisen Rucha Golwalkar Yuliang Ma Andrey Morozov Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

In modern and complex production systems, the focus is shifted toward the software part. software-Defined Manufacturing (SDM) and Cyber-Physical Production Systems (CPPS) characterize this trend. SDM and CPPS enable the concept of adaptive, flexible, and self-configuring production systems. These software-intensive robotic systems are safety-critical because they usually are applied in the same environments as human workers. Therefore they require a continuous risk assessment. The uploading of a new software to the system can change its behavior drastically and therefore, the risk assessment needs to be redone. Key enabling technologies are digital twins, advanced and hybrid risk models, and Model-to-Model (M2M) transformation methods. In this paper, we introduce a new approach to the automated and continuous risk assessment based on Robot Operating System (ROS) code of a software-defined robotic system. The approach pipelines four key elements: (i) a logger that logs the data of the digital twin, (ii) an adder algorithm that creates risk annotated code based on the given ROS code, the output of the logger, and the hardware description including risk data of robot parts, (iii) an M2M transformation algorithm that automatically generates hybrid risk models from risk-annotated code, and (iv) OpenPRA solvers for numerical evaluation of the generated hybrid risk models.

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A Novel Architecture for Robust and Adaptive Machine Learning Using Heterogeneous Data in Condition Monitoring of automation Systems

A Novel Architecture for Robust and Adaptive Machine Learnin...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Simon Kamm Paveen Rajai Suthandhira Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

Machine learning implementations in an industrial setting poses various challenges due to the heterogeneous nature of the data sources. A classical machine learning algorithm cannot adapt to dynamic changes in the environment, such as the addition, removal, or failure of a data source. However, to handle heterogeneous data and the challenges coming with this, it is a mandatory capability to build robust and adaptive machine learning models for industrial applications. In this work, a novel architecture for robust and adaptive machine learning is proposed to address these challenges. For this, an architecture consisting of different modular layers is developed, where different models can be easily plugged in. The architecture can handle heterogeneous data with different fusion techniques, which are discussed and evaluated in this paper. The proposed architecture is then evaluated on two public datasets for condition monitoring of automation systems to prove its robustness and adaptiveness. The architecture is compared with baseline models and shows more robust performance in case of failing/removed data sources. In addition, new data sources can easily be added without the need to retrain the whole model. Furthermore, the architecture can detect and locate faulty data sources.

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Improving Anomaly Detection with Adaptive Dynamic Threshold: A Review and Enhanced Method

Improving Anomaly Detection with Adaptive Dynamic Threshold:...

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International Conference on System Reliability and Science (ICSRS)

作者： Arman Aghaei Attar Kaibin Bao Veit Hagenmeyer Tagir Fabarisov Andrey Morozov Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

In fast-growing modern cyber-physical systems, reliability plays a vital role. Effective anomaly detection, which identifies security and safety issues at early stage, is essential for ensuring system reliability. While much research has focused on anomaly detection techniques, fewer studies address a key challenge, setting precise and responsive thresholds for anomaly detection. In this study, we conduct a comprehensive review of current threshold setting methods. Thereafter, we introduce a novel approach for adaptive threshold setting. Our method is tailored for diverse safety and security tasks and is tested on a safety-critical exoskeleton model and a cybersecurity scenario for energy systems. The results demonstrate that our approach could enhance the threshold setting for anomaly detection in CPS.

关键词： Adaptation models Reviews Exoskeletons Time series analysis Cyber-physical systems Network security Safety Distributed power generation Reliability Anomaly detection

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Continuous Analysis and Optimization of Vehicle software Updates using the Intelligent Digital Twin

Continuous Analysis and Optimization of Vehicle Software Upd...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Matthias Weiß Manuel Müller Falk Dettinger Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

An increasing proportion of software characterizes modern automated systems such as vehicles. It can be used to provide new functions to improve the customer experience in terms of automation, personalization, and connectivity. For system development, this means that classic processes must be rethought and supplemented by new strategies. This is mapped via the trend of the software-defined systems. In this context, individual components can be improved over the entire life cycle. In order for these updates to function properly, testing before deployment is commonplace. However, since the software is complex and it is infeasible to test 100% of the possible scenarios, a software update can still lead to unforeseen side effects. At the same time, it must be monitored whether the desired update effects actually apply. In the worst case, the side effects include errors that must be found as quickly as possible in order not to negatively influence product quality and subsequent innovation cycles. The goal of this work is to detect side effects that manifest themselves as anomalies at runtime and to trace them back to the corresponding code change. For this purpose, a new approach is presented that uses the digital twin of the vehicle as an information base, providing operational data and simulations parallel to operation. The simulation generates synthetic data also containing the rare and risky cases that can then be matched against the operational data to identify anomalies. Once an anomaly is detected, a subsequent analysis follows that relates the anomaly to a causal code sequence. The presented approach is realized using the example of a model vehicle with microcontrollers and corresponding peripherals such as sensors and motors. The model vehicle is simulated using the simulation software SimulIDE, which provides the synthetic data. The evaluation shows that the trace back of side effects to the respective code sequences can successfully be achieved.

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Towards autonomous system: flexible modular production system enhanced with large language model agents

arXiv

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arXiv 2023年

作者： Xia, Yuchen Shenoy, Manthan Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

关键词： Industrial research

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Utilizing ISA-95 in an Industrial Knowledge Graph for Material Flow Simulation - Semantic Model Extensions and Efficient Data Integration 56

Utilizing ISA-95 in an Industrial Knowledge Graph for Materi...

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Listl, Franz Georg Fischer, Jan Sohr, Annelie Dittler, Daniel Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany Technology Department Siemens AG Otto-Hahn-Ring 6 Munich81739 Germany

Despite access to emerging technologies for effective data representation and interoperability between different types of data, many enterprises struggle to use the information and knowledge available in manufacturing data to increase the efficiency and quality of their production. This is especially true for knowledge graphs and semantic web technologies. Nevertheless, breaking down the rigid and historical manufacturing data systems and transferring them to knowledge graphs often involves a lot of manual work for cleaning, mapping, and transforming the data. To address this issue, we have developed a methodology to efficiently utilize ISA-95 in knowledge graphs for manufacturing. The methodology is based on two main building blocks. First, we built an ontology based on the ISA-95 standard and extended it with additional semantic concepts. The second building block is an extract, transform, and load pipeline, to allow an efficient mapping of different data formats into the concepts specified by the ontology, and consequently a fast data integration into the knowledge graph. The created knowledge graph serves as basis for efficient management of material flow simulations. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

关键词： Data Integration Industry 4.0 ISA-95 Knowledge Graph Ontology Semantic Mappin

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