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14th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2020

作者： Lindemann, Benjamin Müller, Timo Vietz, Hannes Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear time-variant system dynamics. The present paper delivers a comprehensive overview of existing LSTM cell derivatives and network architectures for time series prediction. A categorization in LSTM with optimized cell state representations and LSTM with interacting cell states is proposed. The investigated approaches are evaluated against defined requirements being relevant for an accurate time series prediction. These include short-term and long-term memory behavior, the ability for multimodal and multi-step ahead predictions and the according error propagation. Sequence-to-sequence networks with partially conditioning outperform the other approaches, such as bidirectional or associative networks, and are best suited to fulfill the requirements. © 2021 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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Siamese Neural Network-Based Quality-Related Abnormality Detection for Sintering Process

Siamese Neural Network-Based Quality-Related Abnormality Det...

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Chinese automation Congress (CAC)

作者： Weiwei Fan Shiqi Li Chao Jia Jiatao Xiong Hanwen Zhang Haifeng Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Software Engineering and Appraisal Center China Electronics Standardization Institute Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The sintering process serves as a crucial pretreatment unit in ironmaking, supplying the primary raw materials for the blast furnace. Accurately and real-time estimating the quality of the final sintered ore holds significant importance. Nonetheless, the sintering process data is marred by complexities such as nonlinearity, nonstationarity, non-Gaussianity, and dynamic behaviors, which complicate the application of traditional fault detection methods. In this paper, we introduce a novel approach employing Siamese neural networks for quality-related abnormality detection (SNN-QAD) for the sintering process, focusing specifically on detecting abnormalities in the ferrous oxide (FeO) content of sintered ore. Siamese neural networks are initially used to isolate quality-related stationary features, adeptly navigating the data's nonlinear properties. The Wasserstein distance is then applied to gauge the dissimilarity between distributions, tackling the non-Gaussian distribution challenge. These stationary features are subsequently leveraged as input for a regression model, with the FeO content acting as the output variable, to facilitate training. Hotelling's $(\mathrm{T}^{2})$ statistic is employed to identify deviations and pinpoint fault occurrences. The paper culminates with the experimental validation of our method using actual sintering process data.

关键词： Training Fault diagnosis Navigation Neural networks Sintering Focusing Feature extraction Real-time systems Raw materials Complexity theory

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An Overview on Designs and Applications of Context-Aware automation Systems

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Procedia Computer Science 2022年 207卷 2414-2423页

作者： Nada Sahlab Nasser Jazdi Michael Weyrich University of Stuttgart Institute of Industrial Automation and Software Engineering Pfaffenwaldring 47 70569 Stuttgart Germany

automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.

关键词： Systematic Literature Review context-aware automation systems cyber-physical systems knowledge graph

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Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models 54

Enhancing an Intelligent Digital Twin with a Self-organized ...

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54th CIRP Conference on Manufacturing Ssystems, CMS 2021

作者： Müller, Timo Lindemann, Benjamin Jung, Tobias Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

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. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively. © 2021 Elsevier B.V.. All rights reserved.

关键词： Life cycle

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An Improved Algorithm for Facial Image Restoration Based on GAN 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Zhang, Jibo Yuan, Jia Zhang, Dongbo Xiang, Lu School of Software Engineering Software Engineering Institute of Guangzhou Guangzhou510990 China School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce Guangzhou510510 China College of Automation Zhongkai University of Agriculture and Engineering Guangdong Guangzhou510070 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

The paper proposed a two-stage facial image restoration method based on structure and texture network, which includes sketch restore network and texture generation network. The sketch restore network strives to restore the sketch structure in the missing area of the image, and the texture generation network generates the texture information of the missing area based on the sketch structure of the sketch restore network and the pixels of the known area. The method is not only able to successfully generate semantically reasonable and visually realistic content for missing regions, but also allows users to manipulate the structural properties of synthetic content in missing regions. Experimental results show that the proposed method not only has good performance, but also is more flexible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image reconstruction

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Use of the Intelligent Digital Twin for Dynamic Calculation of the Reliability of Industrial automation Systems

Use of the Intelligent Digital Twin for Dynamic Calculation ...

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IEEE International Conference on automation, Quality and Testing, Robotics, AQTR

作者： B. C. Gü l N. Jazdi M. Mü ller Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

The reliability calculation is an important step in the system analysis to determine the remaining service life of the industrial automation system’s components operating in changing environments. In contrast to classic automation systems working without considering dynamic environment parameters, for an accurate calculation, it is not adequate to use only the information obtained from product specifications or experiments. Rather, this calculation should be done dynamically. For this reason, we used the Markov model as the reliability calculation method, since it is a powerful state-based analytical technique considering the dynamic aspects concerning testing and maintenance. This is the need to add dynamism to the system. This paper proposes a novel approach using the concept of intelligent Digital Twin (iDT) for the dynamic calculation of the reliability of industrial automation systems. Moreover, we evaluate our concept as a prototype. Our developed concept is a comprehensive approximation of the calculation of reliability in an industrial automation system by considering dynamic influencing factor failure rate, human factor failure rate, manufacturer failure rate, and repair rate.

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Wearable multichannel-active pressurized pulse sensing platform Yunlong

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Microsystems & Nanoengineering 2024年第3期10卷 283-292页

作者： Zhao Qingxia Sun Shixuan Mei Libo Gao Xikuan Zhang Zekun Yang Xueli Nan Haiyan Zhang Chenyang Xue Junyang Li Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University361102 XiamenChina Discipline of Intelligent Instrument and Equipment Xiamen University361102 XiamenChina Department of Electronic Engineering Ocean University of China266000 QingdaoChina School of Automation and Software Engineering Shanxi University030006 TaiyuanChina Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) 361005 XiamenChina Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education North University of China030051 TaiyuanChina Science and Technology on Vacuum Technology and Physics Laboratory Lanzhou Institute of PhysicsChinese Academy of Space Technology730000 LanzhouChina

With the modernization of traditional Chinese medicine(TCM),creating devices to digitalize aspects of pulse diagnosis has proved to be *** currently available pulse detection devices usually rely on external pressure devices,which are either bulky or poorly integrated,hindering their practical *** this work,we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis *** combines a flexible pressure sensor array,flexible airbag array,active pressure control unit,advanced machine learning approach,and a companion mobile application for human–computer *** to the high sensitivity(460.1 kPa^(−1)),high linearity(R^(2)>0.999)and flexibility of the flexible pressure sensors,the device can accurately simulate finger pressure to collect pulse waves(Cun,Guan,and Chi)at different external pressures on the *** addition,by measuring the change in pulse wave amplitude at different pressures,an individual’s blood pressure status can be successfully *** enables truly wearable,actively pressurized,continuous wireless dynamic monitoring of wrist pulse *** innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

关键词： pulse diagnosis enable

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Research on filtering method of telemetry data based on whale optimization and wavelet transform 5

Research on filtering method of telemetry data based on whal...

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2023 5th International Conference on Information Science, Electrical, and automation engineering, ISEAE 2023

作者： Li, Cheng Wang, Zhang Pu, Fang Chen, Maolin Liu, Ke Gansu Jiuquan 14th Branch Gansu Province Jiuquan City735018 China Institute of Electronic Engineering China Academy of Engineering Physics Sichuan Province Mianyang City621999 China College of Automation and Software Shanxi University Shangxi Province Taiyuan City China

ISBN: (纸本)9781510667440

Ground based telemetry stations are usually used to acquire real-time information of flight vehicles, and monitor the flying states in order to guarantee the safety of flight tests. However, when the telemetry ground station tracks the aerial vehicle, some wild values are inevitably included in the received telemetry data due to various interference factors, which can seriously affect the interpretation of the telemetry data and the evaluation of the vehicle performance. In order to make up for the shortage of existing telemetry data wild value elimination algorithms, this paper uses the wavelet transform threshold method to eliminate the wild values in telemetry data based on the principle of wavelet transform, and introduces the swarm intelligence optimization algorithm to obtain the optimal thresholds for different telemetry data adaptively. The optimal threshold and threshold function coefficients are obtained for different telemetry data to achieve better filtering effect. Corresponding results show that the proposed method can effectively eliminate the wild values in the telemetry data and realize the filtering of the telemetry data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Wavelet transforms

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Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection

arXiv

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arXiv 2022年

作者： Maschler, Benjamin Knodel, Tim Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub)datasets to be clustered sequentially. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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A Model Based Approach for Generating Modular Manufacturing Control Systems software 34

A Model Based Approach for Generating Modular Manufacturing ...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： El Hamlaoui, Mahmoud Qamsane, Yassine Laghouaouta, Youness Mishra, Anant ENSIAS Mohammed V University in Rabat Rabat Morocco Siemens Technology Automation Engineering Software CharlotteNC28273 United States National Institute of Posts and Telecommunications Rabat Morocco

ISBN: (纸本)1891706543

Digitalization is transforming manufacturing systems to become more agile and smart thanks to the integration of sensors and connection technologies that help capture data at all phases of a product's life cycle. Digitalization promises to improve manufacturing flexibility, quality, productivity, and reliability. However, there is still a significant need of effective methods to develop models that could enhance the capabilities of manufacturing systems. Formal methods and tools are becoming essential to achieve this objective. Within this context, this paper introduces a formal software solution for the automatic generation of modular manufacturing control systems software. The proposed solution leverages software Model-Based Design (MBD) techniques to reduce development effort, time, and human error by automating several manual steps. © 2022 Knowledge Systems institute Graduate School. All rights reserved.

关键词： Industry 4.0

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