Wireless charging is an important solution to prolong the lifetime of wireless sensors with limited energy. However, charging efficiency can be greatly affected by the alignment of coils which brings a non-trivial challenge to the control of the mobile charger. In this paper, we implement a wireless charging testbed based on magnetically-coupled resonant wireless power transfer (MCR-WPT). The MCR-WPT module is equipped on a mobile robot to charge wireless sensors. The vision-based wireless charging alignment (V-WCA) algorithm is proposed to use machine vision for coil alignment. Moreover, we propose to use the wireless communication capability of wireless sensors to feedback the charging power during the alignment process, and develop the communication and vision-based wireless charging alignment (CV-WCA) algorithm based on this idea. The experimental results prove that the CV- WCA algorithm is a promising solution to improve the charging efficiency in wireless rechargeable sensor networks.
{"title":"Improving Wireless Charging Efficiency with Machine Vision and Communication for Industrial Wireless Rechargeable Sensor Networks","authors":"Yaxiang Chen, Jingjing Yang, Anguo Liu, Ming-Chia Lai, Zhezhuang Xu, Jingao Hu","doi":"10.1109/INDIN45582.2020.9442213","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442213","url":null,"abstract":"Wireless charging is an important solution to prolong the lifetime of wireless sensors with limited energy. However, charging efficiency can be greatly affected by the alignment of coils which brings a non-trivial challenge to the control of the mobile charger. In this paper, we implement a wireless charging testbed based on magnetically-coupled resonant wireless power transfer (MCR-WPT). The MCR-WPT module is equipped on a mobile robot to charge wireless sensors. The vision-based wireless charging alignment (V-WCA) algorithm is proposed to use machine vision for coil alignment. Moreover, we propose to use the wireless communication capability of wireless sensors to feedback the charging power during the alignment process, and develop the communication and vision-based wireless charging alignment (CV-WCA) algorithm based on this idea. The experimental results prove that the CV- WCA algorithm is a promising solution to improve the charging efficiency in wireless rechargeable sensor networks.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115737270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442238
Douglas A. Goulart, N. D. F. Traversi, J. C. O. Mendonça, R. N. Rodrigues, E. Estrada, Paulo L. J. Drews-Jr, Vinícius M. Oliveira, S. Botelho
The production of food for all the population in the world became the biggest concern. The population continues to grow and the number of farmable lands has been decreasing. To make the lands more productive, fertilizers are used on a larger scale. To guarantee the quality of the product, particle size analysis are made by mechanical sieving. With the time, the wear-out of the sieving in the fertilizer industry the results of the particle size analysis will be erroneous. So the computer vision appears as an alternative that is non-invasive and less time-consuming. In this context, this paper has the objective to develop a grain surface simulator capable of generating virtual images with overlapping grains, since there is a difficulty to obtain annotated data of images of fertilizers. In order to validate the proposed simulator using a DIP algorithm, noises are added in the virtual images to compare with the reality in the industry, to show how well the particle size analysis with computer vision were handled towards adversities. The results of the overlapping analysis show that when the virtual image has a fewer number of grains, the DIP algorithm can identify the majority of grains, consequently with less error in the particle size analysis. Different noises, at different intensities, have their effects analyzed on the algorithm. As the analyzes in this study match with the reality showing the consequences, tendencies, and errors of the overlapping of grains and noises in the images, the simulator developed here matches with reality and is extremely useful to facilitate the study of complex cases of application of visual computing and digital image processing in particle size analysis of fertilizers.
{"title":"Grain Surface Simulator to Averiguate the Overlapping and Noise Problems on Computer Vision Granullometry of Fertilizers","authors":"Douglas A. Goulart, N. D. F. Traversi, J. C. O. Mendonça, R. N. Rodrigues, E. Estrada, Paulo L. J. Drews-Jr, Vinícius M. Oliveira, S. Botelho","doi":"10.1109/INDIN45582.2020.9442238","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442238","url":null,"abstract":"The production of food for all the population in the world became the biggest concern. The population continues to grow and the number of farmable lands has been decreasing. To make the lands more productive, fertilizers are used on a larger scale. To guarantee the quality of the product, particle size analysis are made by mechanical sieving. With the time, the wear-out of the sieving in the fertilizer industry the results of the particle size analysis will be erroneous. So the computer vision appears as an alternative that is non-invasive and less time-consuming. In this context, this paper has the objective to develop a grain surface simulator capable of generating virtual images with overlapping grains, since there is a difficulty to obtain annotated data of images of fertilizers. In order to validate the proposed simulator using a DIP algorithm, noises are added in the virtual images to compare with the reality in the industry, to show how well the particle size analysis with computer vision were handled towards adversities. The results of the overlapping analysis show that when the virtual image has a fewer number of grains, the DIP algorithm can identify the majority of grains, consequently with less error in the particle size analysis. Different noises, at different intensities, have their effects analyzed on the algorithm. As the analyzes in this study match with the reality showing the consequences, tendencies, and errors of the overlapping of grains and noises in the images, the simulator developed here matches with reality and is extremely useful to facilitate the study of complex cases of application of visual computing and digital image processing in particle size analysis of fertilizers.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects.
{"title":"Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions","authors":"Qi Li, Shuangjie Liu, Bingru Yang, Yiyun Xu, Liang Chen, Changqing Shen","doi":"10.1109/INDIN45582.2020.9442084","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442084","url":null,"abstract":"Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123782941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442139
Peng Ding, M. Jia
Health evaluation is attracting more and more attention in the domain of machinery prognostic and health management (PHM). Meanwhile, few studies have been devoted to health evaluation under variable working conditions and few shots learning, which are common situations under industrial sites. Thus, this shortcoming becomes the motivation of our study. We propose subspace meta-learning (SML) that integrates the strengths of knowledge transfer, constructing the statistically relevant latent subspace, and meta learning, realizing few shots prognostics. To be specifically, time-frequency images are first extracted with sliding windows along with the vibration signals across different life experiments of rolling bearings. Then, two-dimensional domain adaptation based on high order statistical properties is utilized to construct latent subspace and generate meta degradation knowledge. Finally, the convolutional layer based meta learning under model-agnostic learning mode is set up based on the time-frequency degradation knowledge. For a transparent test of our proposed SML health evaluation methodologies, public FEMTO-ST bearing datasets are employed for verifications, and comparisons are also conducted between existing prediction methods. Prediction performances reveal that the superiority of SML under few-shot prognostics.
{"title":"Intelligent health evaluation of rolling bearings based on subspace meta-learning","authors":"Peng Ding, M. Jia","doi":"10.1109/INDIN45582.2020.9442139","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442139","url":null,"abstract":"Health evaluation is attracting more and more attention in the domain of machinery prognostic and health management (PHM). Meanwhile, few studies have been devoted to health evaluation under variable working conditions and few shots learning, which are common situations under industrial sites. Thus, this shortcoming becomes the motivation of our study. We propose subspace meta-learning (SML) that integrates the strengths of knowledge transfer, constructing the statistically relevant latent subspace, and meta learning, realizing few shots prognostics. To be specifically, time-frequency images are first extracted with sliding windows along with the vibration signals across different life experiments of rolling bearings. Then, two-dimensional domain adaptation based on high order statistical properties is utilized to construct latent subspace and generate meta degradation knowledge. Finally, the convolutional layer based meta learning under model-agnostic learning mode is set up based on the time-frequency degradation knowledge. For a transparent test of our proposed SML health evaluation methodologies, public FEMTO-ST bearing datasets are employed for verifications, and comparisons are also conducted between existing prediction methods. Prediction performances reveal that the superiority of SML under few-shot prognostics.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126952806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, intelligent fault diagnosis has achieved fruitful research results. However, the small sample is still the major problem in fault diagnosis owing to lacking fault data of machines. In view of this, a signals augmented semi-supervised learning scheme is proposed for intelligent fault diagnosis in the case of small sample. In the proposed method, fault signal samples are generated by generative adversarial networks (GAN). The fault classifier is trained in a semi-supervised way using the generated samples and a small number of real samples. Besides, attention mechanism is applied in the fault classifier for sensitive feature extraction. The trained fault classifier is capable of accurate fault classification. Results indicate that the proposed method is effective in mechanical fault diagnosis under the small sample condition.
{"title":"Towards Intelligent Fault Diagnosis under Small Sample Condition via A Signals Augmented Semi-supervised Learning Framework","authors":"Tianci Zhang, Jinglong Chen, Tongyang Pan, Zitong Zhou","doi":"10.1109/INDIN45582.2020.9442224","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442224","url":null,"abstract":"Recently, intelligent fault diagnosis has achieved fruitful research results. However, the small sample is still the major problem in fault diagnosis owing to lacking fault data of machines. In view of this, a signals augmented semi-supervised learning scheme is proposed for intelligent fault diagnosis in the case of small sample. In the proposed method, fault signal samples are generated by generative adversarial networks (GAN). The fault classifier is trained in a semi-supervised way using the generated samples and a small number of real samples. Besides, attention mechanism is applied in the fault classifier for sensitive feature extraction. The trained fault classifier is capable of accurate fault classification. Results indicate that the proposed method is effective in mechanical fault diagnosis under the small sample condition.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122020638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442248
Hao Zhong, Zhongbao Wei, Hongwen He
Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) attributed to their advantages of high energy density and long cycle life. In this vision, fast charging of the LIB system has been a crucial technology to promote the large-scale penetration of EVs in the existing automotive market. Motivated by this, a thermal-constrained fast charging method is proposed based on the model predictive control (MPC) concept in this paper. A coupled electro-thermal model is established, based on which two model-based observers are devised to estimate the state of charge (SOC) and internal temperature of LIB. On this premise, an MPC-based controller is exploited to trade-off smartly the charging fastness and the physical constraints. Comparative results show that the proposed method can optimize the charging towards high speed while keep the terminal voltage and battery internal temperature both within the safety region, which forms an obvious superiority over the traditionally-used constant-current-constant-voltage (CC-CV) protocol.
{"title":"Predictive Fast Charging of Lithium-ion Battery with Electro-thermal Constraints","authors":"Hao Zhong, Zhongbao Wei, Hongwen He","doi":"10.1109/INDIN45582.2020.9442248","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442248","url":null,"abstract":"Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) attributed to their advantages of high energy density and long cycle life. In this vision, fast charging of the LIB system has been a crucial technology to promote the large-scale penetration of EVs in the existing automotive market. Motivated by this, a thermal-constrained fast charging method is proposed based on the model predictive control (MPC) concept in this paper. A coupled electro-thermal model is established, based on which two model-based observers are devised to estimate the state of charge (SOC) and internal temperature of LIB. On this premise, an MPC-based controller is exploited to trade-off smartly the charging fastness and the physical constraints. Comparative results show that the proposed method can optimize the charging towards high speed while keep the terminal voltage and battery internal temperature both within the safety region, which forms an obvious superiority over the traditionally-used constant-current-constant-voltage (CC-CV) protocol.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128484896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442241
Xiuxia Yin, Zhiwei Gao, Yichuan Fu
This article concerns observer-based consensus compensation control for heterogeneous networked multi-agent systems under both networked Denial of Service (DoS) attacks and transmission delays. We propose a control method that combines the observer-based adaptive event-triggered control and the observer-based cloud predictive control, which can not only reduce the network transmission burden, but also can compensate for the negative effects caused by DoS attacks and transmission delays completely. The consensus conditions, the observer and controller gain matrices and the event-triggering parameter matrices are all simultaneously derived by using the linear matrix inequality method.
{"title":"Observer-based event-triggered cloud predictive control for heterogeneous MASs with DoS attacks and delays","authors":"Xiuxia Yin, Zhiwei Gao, Yichuan Fu","doi":"10.1109/INDIN45582.2020.9442241","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442241","url":null,"abstract":"This article concerns observer-based consensus compensation control for heterogeneous networked multi-agent systems under both networked Denial of Service (DoS) attacks and transmission delays. We propose a control method that combines the observer-based adaptive event-triggered control and the observer-based cloud predictive control, which can not only reduce the network transmission burden, but also can compensate for the negative effects caused by DoS attacks and transmission delays completely. The consensus conditions, the observer and controller gain matrices and the event-triggering parameter matrices are all simultaneously derived by using the linear matrix inequality method.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123872603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442127
B. Wang, W. Peng, G. Feng, X. Zhang, Y. Wang, U. Manandhar
This paper proposes a bidirectional multiple-port three-level (BMPTL) DC-DC converter with two-stage structure for the hybrid energy storage system (HESS). The advantages of the proposed converter over the conventional converters for HESSs including reduced component size, superior extension capability and better control flexibility. These advantages results from the two-stage structure, the availability of three voltage levels, and the multiple-port design. A new control method based on deadbeat control strategy has been developed to regulate the BMPTL DC-DC converter. The proposed deadbeat-based method can mitigate the power imbalanced in DC microgrid and allocate proper power assignment for the battery and supercapacitor according to their characteristics simultaneously. To conduct the verification, a DC microgrid simulation model including the PV, load and HESS interfaced by the proposed BMPTL DC-DC converter is built. The simulation results are discussed in detail to demonstrate the effectiveness of the proposed BMPTL DC-DC converter with the deadbeat-based method.
{"title":"Bidirectional Multiple-Port Three-Level DC-DC Converter for HESS in DC Microgrids","authors":"B. Wang, W. Peng, G. Feng, X. Zhang, Y. Wang, U. Manandhar","doi":"10.1109/INDIN45582.2020.9442127","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442127","url":null,"abstract":"This paper proposes a bidirectional multiple-port three-level (BMPTL) DC-DC converter with two-stage structure for the hybrid energy storage system (HESS). The advantages of the proposed converter over the conventional converters for HESSs including reduced component size, superior extension capability and better control flexibility. These advantages results from the two-stage structure, the availability of three voltage levels, and the multiple-port design. A new control method based on deadbeat control strategy has been developed to regulate the BMPTL DC-DC converter. The proposed deadbeat-based method can mitigate the power imbalanced in DC microgrid and allocate proper power assignment for the battery and supercapacitor according to their characteristics simultaneously. To conduct the verification, a DC microgrid simulation model including the PV, load and HESS interfaced by the proposed BMPTL DC-DC converter is built. The simulation results are discussed in detail to demonstrate the effectiveness of the proposed BMPTL DC-DC converter with the deadbeat-based method.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-20DOI: 10.1109/INDIN45582.2020.9442083
Mahyar Azarmipour, Haitham Elfaham, Caspar Gries, T. Kleinert, U. Epple
Industrial automation architectures have been evolving due to the new requirements in context of Industry 4.0 or other similar paradigms. Interconnectedness and modularity are two factors that play a crucial role in fulfilling these new requirements. The features must be applied to different automation levels and domains. This paper focuses on a service based interaction of MES and process control in a modular an interconnected environment to promote process optimization. Another important factor that must be considered is security. Linking the industrial automation with IT technologies is a basis for optimization and digitalization but it exposes the system to security threats. Retrieving process information for further data processing and online system optimization including the controlled process must be accomplished via an approach that ensures the security requirements of the system. This paper discusses such a secure approach for the interaction between process control, MES and a digital twin application.
{"title":"A Service-based Architecture for the Interaction of Control and MES Systems in Industry 4.0 Environment","authors":"Mahyar Azarmipour, Haitham Elfaham, Caspar Gries, T. Kleinert, U. Epple","doi":"10.1109/INDIN45582.2020.9442083","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442083","url":null,"abstract":"Industrial automation architectures have been evolving due to the new requirements in context of Industry 4.0 or other similar paradigms. Interconnectedness and modularity are two factors that play a crucial role in fulfilling these new requirements. The features must be applied to different automation levels and domains. This paper focuses on a service based interaction of MES and process control in a modular an interconnected environment to promote process optimization. Another important factor that must be considered is security. Linking the industrial automation with IT technologies is a basis for optimization and digitalization but it exposes the system to security threats. Retrieving process information for further data processing and online system optimization including the controlled process must be accomplished via an approach that ensures the security requirements of the system. This paper discusses such a secure approach for the interaction between process control, MES and a digital twin application.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121460721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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