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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Hildebrandt, Gary Habiger, Pascal Dittler, Daniel Drath, Rainer Weyrich, Michael Pforzheim University of Applied Sciences Institute of Smart Systems and Services Pforzheim Germany Stuttgart University Institute of Industrial Automation and Software Engineering Stuttgart Germany

ISBN: (纸本)9798350339918

In the era of Industry 4.0, data is becoming increasingly vital for future manufacturing and digital twin applications. Currently, primarily asset-created data is utilized, while the potential benefits of incorporating external data from connected production systems remain untapped. This underutilization is largely due to the complex and time-consuming nature of integrating external data. Automating this process could significantly improve efficiency and unlock valuable insights for more effective decision-making. In this paper, we present the requirements for a system aimed at automating the integration of external data, considering factors such as data heterogeneity and the need to support diverse communication protocols. We further introduce an assistant system that facilitates this automated integration, enhancing manufacturing processes and digital twin applications. Furthermore, we provide a prototypical implementation of the assistant system in the context of a collaborative robot utilizing information from its surroundings obtained through external camera and LiDAR imaging. By addressing the challenges of external data integration, this research contributes to the advancement of data-driven manufacturing and the optimization of digital twin technologies. © 2023 IEEE.

关键词： Internet of things

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Dynamic Production Scheduling with Intelligent Products in a Modular Production System 28

Dynamic Production Scheduling with Intelligent Products in a...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Artelt, Maurice Dittler, Daniel Hildebrandt, Gary Braun, Dominik Jazdi, Nasser Weyrich, Michael University of Stuttgart Institute of Industrial Automation and Software Engineering Stuttgart Germany Pforzheim University for Applied Sciences Institute of Smart Systems and Services Pforzheim Germany

ISBN: (纸本)9798350339918

Industrial automation is driven by trends such as autonomy, intelligence and networking. Increasing energy demands, scarce resources and shorter product lifecycles pose challenges to production systems such as interoperability, flexibility and extensibility. The concept of the Digital Twin, acting as a virtual representation of a production system, can address these challenges. This paper presents a concept that realizes a dynamic production scheduling as partial function of the Digital Twin using the Asset Administration Shell and a service-oriented architecture. The authors specifically address automated asset production scheduling and propose an MQTT broker with a Functionality Dictionary server. Through the Asset Administration Shell interface, the proposed production scheduler communicates with the proposed product calendar to generate a specialized production schedule for each product. The evaluation scenario in a cyber-physical laboratory shows the advantages of this concept. © 2023 IEEE.

关键词： Memory architecture

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Digital Twin to Enhance Offshore Power-to-X Platforms with Operational Alarm Management 57

Digital Twin to Enhance Offshore Power-to-X Platforms with O...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Bodenstein, Frederike Dieckmann, Stefan Dittler, Daniel Geschke, Alexander Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße Magdeburg39106 Germany

The operation of innovative process plants like offshore Power-to-X platforms that produces green hydrogen and derivatives poses critical risks for the environment and personnel as well as the operating company. This is due to the high system complexity, the energies and materials used and the associated high investment and maintenance costs. To keep these risks low, the occurrence of corresponding critical cases must be avoided, for which reason the personnel must be optimally assisted and enabled in their tasks during all phases of the platform life cycle. We have therefore enhanced a digital twin presenting a teleoperation platform that allows modular use of assistance systems. As a proof of concept, an alarm management system is presented. © 2024 The Authors.

关键词： Investments

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Data Augmentation for Vulnerability Detection Based on Code Refactoring and Mixup 35

Data Augmentation for Vulnerability Detection Based on Code ...

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35th IEEE International Symposium on software Reliability engineering Workshops, ISSREW 2024

作者： Xing, Ying Huang, Jiaqi Wang, Guilong You, Yehao Yang, Bin Li, Xiaofeng Luo, Yixing Beijing University of Posts and Telecommunications School of Intelligent Engineering and Automation Beijing China Beijing University of Posts and Telecommunications School of Artifcial Intelligence Beijing China Yunnan Key Laboratory of Software Engineering Yunnan China China Unicom Research Institute Beijing China Beijing Institute of Control Engineering Software Development Center Beijing China

ISBN: (纸本)9798350367041

In recent years, the number of software vulnerabilities has gradually increased, posing a threat to the security of software, and identifying whether there are vulnerabilities in software is crucial for assuring its quality. With the development of deep learning, vulnerability detection based on deep learning has achieved good results. However, real-world software vulnerability datasets often suffer from class imbalance and a lack of labeled data, limiting the performance of deep learning models. Therefore, this paper proposes a data augmentation method to alleviate these two problems. Firstly, we treats samples with vulnerabilities in the dataset as code, and generates more samples with vulnerabilities through code refactoring to alleviate class imbalance issues. Secondly, we use the mixup method to perform linear interpolation on the embedding vectors which represent codes, and add the interpolated vectors into the training set, thereby increasing the amount of labeled training data. To evaluate our method, we conducted experiments on three projects of the Promise dataset. The experimental results show that our data augmentation method can effectively improve the effect of vulnerability detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Stationary Feature Extract and Process Monitoring Based on Autoencoder 42

Stationary Feature Extract and Process Monitoring Based on A...

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42nd Chinese Control Conference, CCC 2023

作者： Fan, Weiwei Tang, Wanqing Zhang, Hanwen Jia, Chao Liang, Juntao He, Jie School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China China Electronics Standardization Institute Software Engineering and Appraisal Center Beijing China

ISBN: (纸本)9789887581543

To monitor non-stationary industrial processes, a stationary feature extract method is proposed, which can be regarded as a nonlinear version of traditional stationary subspace analysis. To enhance feature extraction, the linear transformation in stationary subspace analysis is replaced by an autoencoder. To extract the stationary features, an additional term is introduced into the loss function of the autoencoder, which measures the consistency of the feature distributions over different time periods. The convex hull based on the stationary features is used to construct a monitoring statistic for the alarm of abnormal working conditions. A case study based on an ironmaking process illustrates that the proposed method can effectively achieve abnormality detection. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Process monitoring

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Concept: Dynamic Risk Assessment for AI-Controlled Robotic Systems

arXiv

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arXiv 2024年

作者： Grimmeisen, Philipp Sautter, Friedrich Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

AI-controlled robotic systems pose a risk to human workers and the environment. Classical risk assessment methods cannot adequately describe such black box systems. Therefore, new methods for a dynamic risk assessment of such AI-controlled systems are required. In this paper, we introduce the concept of a new dynamic risk assessment approach for AI-controlled robotic systems. The approach pipelines five blocks: (i) a Data Logging that logs the data of the given simulation, (ii) a Skill Detection that automatically detects the executed skills with a deep learning technique, (iii) a Behavioral Analysis that creates the behavioral profile of the robotic systems, (iv) a Risk Model Generation that automatically transforms the behavioral profile and risk data containing the failure probabilities of robotic hardware components into advanced hybrid risk models, and (v) Risk Model Solvers for the numerical evaluation of the generated hybrid risk models. © 2024, CC BY.

关键词： Reinforcement learning

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Hybrid Approaches and Datasets for Remaining Useful Life Prediction: A Review

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Procedia CIRP 2024年 130卷 294-300页

作者： Maurice Artelt Matthias Weiß Daniel Dittler Yannick Goersch Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 70569 Stuttgart Germany

The prediction of the remaining service life is a widely researched topic. A precise forecast of the remaining service life enables optimising the total cost of ownership, resulting in better utilization of the resources already used as well as the avoidance of cost-intensive unplanned downtimes. Especially in the increasingly important second life movement for resources that have already been produced, the accurate prediction of the remaining service life is a decisive factor. Data is always required for modelling a prediction. On the one hand, these can be used for the classic determination of tabular book values for physical models. On the other hand, it can also be used for a data science project to create a data-driven modelling approach. This paper presents the results of the literature review on the use of methods for data-driven prediction of remaining service life. It considers which methods were used in the individual papers as well as the statements about the underlying database. Therefore, the scope of this survey is to explore recent data-driven approaches for remaining useful life prediction. As such, “remaining service life” was used as a keyword and the abstracts were screened for the modelling approaches and data used. Subsequently, this key information was categorized and evaluated. The results of the literature review show that the majority of papers use data-driven modelling to predict the remaining service life being dominated by LSTM, CNN and particle filtering, followed by physical modelling. Most papers use a public dataset to showcase their research results like the well-known C-MAPSS, bearing or Lithium-Ion Battery dataset. The minority uses hybrid modelling. The on this field published paper favoured addressing data scarcity, mitigate overfitting and improve prediction accuracy.

关键词： Remaining Useful Life data-driven models physical models survey

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Tackling Erosion in Variant-Rich software Systems: Challenges and Approaches

arXiv

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arXiv 2024年

作者： Stümpfle, Johannes Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

software product lines (SPL) have emerged as a pivotal paradigm in software engineering, enabling the efficient development of variant-rich software systems. Consistently updating these systems, often through over-the-air updates, enables the continuous integration of new features and bug fixes, ensuring the system remains up to date throughout its entire lifecycle. However, evolving such complex systems is an error prone task, leading to a phenomenon known as erosion. This phenomenon significantly impacts the efficiency and longevity of software systems, presenting a formidable challenge for manufacturers of variant-rich software systems, such as in the automotive domain. While existing studies concentrate on the evolutionary planning of variant-rich software systems, there is a noticeable lack of research addressing the problem of erosion. In this paper, we conduct an in-depth exploration of the erosion phenomena within variant-rich software systems. We begin by highlighting the significance of controlling erosion in extensive variant-rich software systems. Subsequently, we address the current challenges regarding tackling erosion, including issues such as the lack of a consensus on understanding and defining erosion, as well as the early detection and elimination. Finally, we outline a first approach aimed at tackling erosion in variant-rich software systems. © 2024, CC BY-NC-ND.

关键词： Erosion

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Three-dimensional reconstruction of industrial parts from a single image

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 340-351页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Key Laboratory of Aeronautics Smart Manufacturing Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.

关键词： Three-dimensional reconstruction Non-uniform rational B-splines Industrial parts Deep learning

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Non-Autoregressive Sign Language Translation: A Transformer Encoder-only Approach for Enhanced Multimodal Integration 24

Non-Autoregressive Sign Language Translation: A Transformer ...

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2024 International Conference on Advanced Robotics, automation engineering and Machine Learning, ARAEML 2024

作者： Du, Yao Xie, Pan Peng, Taiying Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717116

Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-decoder architecture, employing an autoregressive (AR) approach for SLT. This one-by-one token generation method has a slow speed to translate. We design a Non-Autoregressive Sign Language Translation (NA-SLT) model based on Transformer Encoder. The proposed model consists of three components: The text-encoder, sign-encoder, and sign-Text-encoder. Both the text-encoder and sign-encoder utilize the Encoder to model single-modality data. The sign-Text-encoder effectively engages with multi-modal data, producing high-quality corresponding text after minimal refinement iterations. Moreover, during model training, we devise a double-mask strategy. In addition to traditional random masking, the schedule allocates mask probabilities based on each word's frequency, ensuring long-Tail tokens have an increased chance of being masked and receiving extensive training. We evaluate the model on PHOENIX-2014T, and the results indicate that NA-SLT approaches the performance of AR models on BLEU (21.06) and ROUGE(49.59) metrics while significantly improving translation speed (5.4x-7.9x). © 2024 ACM.

关键词： Metadata

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