Pub Date : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972168
Francesco Branz, R. Antonello, F. Tramarin, Tommaso Fedullo, S. Vitturi, L. Schenato
Networked embedded systems for industrial control based on wireless support provide several advantages over wired counterparts, but often reveal unsuitable for the most demanding industrial control applications, such as advanced manufacturing and cooperative robotics. Data exchange over IEEE 802.11 networks may theoretically represent an appropriate solution for time–critical applications, provided that unreliability and non– determinism issues are properly handled. In this respect, this paper hence proposes an original solution, based on a cross– layer approach, to allow the realization of high–speed industrial control–over–Wi-Fi networked embedded systems. The proposal implements a novel robust frame–delay state estimator, a time efficient communication policy, and a specific tuning of critical protocol parameters. Suitable hardware–in–the–loop experiments have been carried out implemented exploiting two different embedded systems. Preliminary results show that the proposed architecture enables industrial control applications requiring a sampling rate of up to 1 kHz, even in presence of non negligible communication errors.
基于无线支持的工业控制网络嵌入式系统提供了与有线系统相比的几个优势，但通常不适合最苛刻的工业控制应用，例如先进制造和协作机器人。在理论上，IEEE 802.11网络上的数据交换可能是时间关键型应用程序的适当解决方案，前提是不可靠性和非确定性问题得到妥善处理。在这方面，本文因此提出了一种基于跨层方法的原创解决方案，以允许实现高速工业控制- wi - fi网络嵌入式系统。该方案实现了一种新颖的鲁棒帧延迟状态估计器，一种时间高效的通信策略，以及对关键协议参数的特定调优。利用两种不同的嵌入式系统进行了相应的硬件在环实验。初步结果表明，即使在存在不可忽略的通信错误的情况下，所提出的架构也可以实现需要高达1khz采样率的工业控制应用。
{"title":"Embedded systems for time–critical applications over Wi-Fi: design and experimental assessment","authors":"Francesco Branz, R. Antonello, F. Tramarin, Tommaso Fedullo, S. Vitturi, L. Schenato","doi":"10.1109/INDIN41052.2019.8972168","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972168","url":null,"abstract":"Networked embedded systems for industrial control based on wireless support provide several advantages over wired counterparts, but often reveal unsuitable for the most demanding industrial control applications, such as advanced manufacturing and cooperative robotics. Data exchange over IEEE 802.11 networks may theoretically represent an appropriate solution for time–critical applications, provided that unreliability and non– determinism issues are properly handled. In this respect, this paper hence proposes an original solution, based on a cross– layer approach, to allow the realization of high–speed industrial control–over–Wi-Fi networked embedded systems. The proposal implements a novel robust frame–delay state estimator, a time efficient communication policy, and a specific tuning of critical protocol parameters. Suitable hardware–in–the–loop experiments have been carried out implemented exploiting two different embedded systems. Preliminary results show that the proposed architecture enables industrial control applications requiring a sampling rate of up to 1 kHz, even in presence of non negligible communication errors.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115360002","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972143
D. Chivilikhin, Sandeep Patil, Anthony Cordonnier, V. Vyatkin
Today, more and more industries are considering moving towards being Industry 4.0 compliant. But this transition is not straightforward due to many reasons: in particular, transfer to new system can lead to significant production downtime and could result in delays and cost overruns. The best way is systematic seamless transition to newer and advanced technologies that Industry 4.0 offers. This paper proposes an automated synthesis framework that learns the behavior of existing legacy and often black-box programmable logic controllers and generates state machines that can be incorporated into IEC 61499 function blocks. The paper presents the toolchain architecture and exemplifies it on a laboratory scale Festo didactic mechatronic system.
{"title":"Towards automatic state machine reconstruction from legacy PLC using data collection","authors":"D. Chivilikhin, Sandeep Patil, Anthony Cordonnier, V. Vyatkin","doi":"10.1109/INDIN41052.2019.8972143","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972143","url":null,"abstract":"Today, more and more industries are considering moving towards being Industry 4.0 compliant. But this transition is not straightforward due to many reasons: in particular, transfer to new system can lead to significant production downtime and could result in delays and cost overruns. The best way is systematic seamless transition to newer and advanced technologies that Industry 4.0 offers. This paper proposes an automated synthesis framework that learns the behavior of existing legacy and often black-box programmable logic controllers and generates state machines that can be incorporated into IEC 61499 function blocks. The paper presents the toolchain architecture and exemplifies it on a laboratory scale Festo didactic mechatronic system.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115691245","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972298
Luis Variz, Luis Piardi, P. J. Rodrigues, P. Leitão
Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, artificial intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decen-tralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This paper discusses the application of supervised Machine Learning techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the quality control of Human Machine Interface (HMI) consoles, equipped with pressure buttons and LCD displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.
{"title":"Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station","authors":"Luis Variz, Luis Piardi, P. J. Rodrigues, P. Leitão","doi":"10.1109/INDIN41052.2019.8972298","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972298","url":null,"abstract":"Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, artificial intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decen-tralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This paper discusses the application of supervised Machine Learning techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the quality control of Human Machine Interface (HMI) consoles, equipped with pressure buttons and LCD displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123086436","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972238
Olatz De Miguel Lázaro, Wael M. Mohammed, Borja Ramis, Ronal Bejarano, J. Lastra
One of the major objectives of international projects in the field of Industrial Automation is to achieve a proper and safe human-robot collaboration. This will permit the coexistence of both humans and robots at factory shop floors, where each one has a clear role along the industrial processes. It’s a matter of fact that machines, including robots, have specific features that determine the kind of operation(s) that they can perform better. Similarly, human operators have a set of skills and knowledge that permits them to accomplish their tasks at work. This article proposes the adaptation of robots to the skills of human operators in order to implement an efficient, safe and comfortable synergy between robots and humans that are working at the same workspace. As a representative case of study, this research work describes an approach for adapting a cobot workstation to human operators within an installed deep learning camera on the cobot. First, the camera is used to recognize the human operator that collaborates with the robot. Then, the corresponding profile is processed and serves as an input to a module in charge of adapting specific features of the robot. In this manner, the robot can adapt e.g., to the speed of operation according to the skills of the worker or deliver parts to be manipulated according to the handedness of the human worker. In addition, the deep learning camera is used for stopping the process at any time that the worked leaves unexpectedly the workstation.
{"title":"An Approach for adapting a Cobot Workstation to Human Operator within a Deep Learning Camera","authors":"Olatz De Miguel Lázaro, Wael M. Mohammed, Borja Ramis, Ronal Bejarano, J. Lastra","doi":"10.1109/INDIN41052.2019.8972238","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972238","url":null,"abstract":"One of the major objectives of international projects in the field of Industrial Automation is to achieve a proper and safe human-robot collaboration. This will permit the coexistence of both humans and robots at factory shop floors, where each one has a clear role along the industrial processes. It’s a matter of fact that machines, including robots, have specific features that determine the kind of operation(s) that they can perform better. Similarly, human operators have a set of skills and knowledge that permits them to accomplish their tasks at work. This article proposes the adaptation of robots to the skills of human operators in order to implement an efficient, safe and comfortable synergy between robots and humans that are working at the same workspace. As a representative case of study, this research work describes an approach for adapting a cobot workstation to human operators within an installed deep learning camera on the cobot. First, the camera is used to recognize the human operator that collaborates with the robot. Then, the corresponding profile is processed and serves as an input to a module in charge of adapting specific features of the robot. In this manner, the robot can adapt e.g., to the speed of operation according to the skills of the worker or deliver parts to be manipulated according to the handedness of the human worker. In addition, the deep learning camera is used for stopping the process at any time that the worked leaves unexpectedly the workstation.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115754868","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972130
I. Buzhinsky
One of the challenges of requirements engineering is the fact that requirements are often formulated in natural language. This represents difficulty if requirements must be processed by formal approaches, especially if these approaches are intended to check the requirements. In model checking, a formal technique of verification by exhaustive state space exploration, requirements must be stated in formal languages (most commonly, temporal logics) which are essentially supersets of the Boolean propositional logic. Translation of natural language requirements to these languages is a process which requires much knowledge and expertise in model checking as well the ability to correctly understand these requirements, and hence automating this process is desirable. This paper reviews existing approaches of requirements formalization that are applicable for, or at least can be adapted to generation of discrete time temporal logic requirements. Based on the review, conclusions are made regarding the practical applicability of these approaches for the considered problem.
{"title":"Formalization of natural language requirements into temporal logics: a survey","authors":"I. Buzhinsky","doi":"10.1109/INDIN41052.2019.8972130","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972130","url":null,"abstract":"One of the challenges of requirements engineering is the fact that requirements are often formulated in natural language. This represents difficulty if requirements must be processed by formal approaches, especially if these approaches are intended to check the requirements. In model checking, a formal technique of verification by exhaustive state space exploration, requirements must be stated in formal languages (most commonly, temporal logics) which are essentially supersets of the Boolean propositional logic. Translation of natural language requirements to these languages is a process which requires much knowledge and expertise in model checking as well the ability to correctly understand these requirements, and hence automating this process is desirable. This paper reviews existing approaches of requirements formalization that are applicable for, or at least can be adapted to generation of discrete time temporal logic requirements. Based on the review, conclusions are made regarding the practical applicability of these approaches for the considered problem.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117195108","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972295
An Ngoc Lam, Øystein Haugen
Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.
{"title":"Applying semantics into Service-oriented IoT Framework","authors":"An Ngoc Lam, Øystein Haugen","doi":"10.1109/INDIN41052.2019.8972295","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972295","url":null,"abstract":"Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127164557","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972062
Yi Deng, Ming Zhan, Meng Wang, Chao Yang, Xiaohong Luo, Jie Zeng, Jing Guo
In the field of industrial automation research, wireless control in key application scenarios has become a research hotspot. Nevertheless, transmission over wireless channels in industrial environments is prone to interference, resulting in frequent erroneous packet deliveries. Forward Error Correction (FEC) code as an approach is able to effectively improve reliability and reduce the number of retransmissions. Therefore, channel coding needs further analysis to achieve better industrial wireless application. To this aim, this paper compares the decoding performance of convolutional codes and low-density parity-check (LDPC) codes on the condition of short packet transmission. The metrics employed for evaluation are bit error rate (BER) and packet error rate (PER). The logarithmic belief propagation (Log-BP) algorithm and the Viterbi decoding algorithm are adopted to LDPC codes and convolutional codes respectively. The results show that the decoding algorithm of LDPC codes is prominent in short packet transmission.
{"title":"Comparing Decoding Performance of LDPC Codes and Convolutional Codes for Short Packet Transmission","authors":"Yi Deng, Ming Zhan, Meng Wang, Chao Yang, Xiaohong Luo, Jie Zeng, Jing Guo","doi":"10.1109/INDIN41052.2019.8972062","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972062","url":null,"abstract":"In the field of industrial automation research, wireless control in key application scenarios has become a research hotspot. Nevertheless, transmission over wireless channels in industrial environments is prone to interference, resulting in frequent erroneous packet deliveries. Forward Error Correction (FEC) code as an approach is able to effectively improve reliability and reduce the number of retransmissions. Therefore, channel coding needs further analysis to achieve better industrial wireless application. To this aim, this paper compares the decoding performance of convolutional codes and low-density parity-check (LDPC) codes on the condition of short packet transmission. The metrics employed for evaluation are bit error rate (BER) and packet error rate (PER). The logarithmic belief propagation (Log-BP) algorithm and the Viterbi decoding algorithm are adopted to LDPC codes and convolutional codes respectively. The results show that the decoding algorithm of LDPC codes is prominent in short packet transmission.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261414","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972050
Bernhard Wally, J. Vyskočil, Petr Novák, C. Huemer, R. Šindelář, Petr Kadera, Alexandra Mazak, M. Wimmer
Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.
{"title":"Production Planning with IEC 62264 and PDDL","authors":"Bernhard Wally, J. Vyskočil, Petr Novák, C. Huemer, R. Šindelář, Petr Kadera, Alexandra Mazak, M. Wimmer","doi":"10.1109/INDIN41052.2019.8972050","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972050","url":null,"abstract":"Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127522467","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972265
Xiang Yin, Jinhua She, Zhentao Liu, Min Wu, Daiki Sato, K. Hirota
A method to suppress exogenous disturbances in a nonlinear system is presented based on equivalent-input-disturbance (EID) approach. The nonlinear term is considered to be useful and should not be rejected in this paper. The method has two exceptional merits: it is a output-feedback control method, and the only restriction of the nonlinear term is used to ensure the stability of the NEID-based control system. Finally, the design procedure is illustrated by a numerical example.
{"title":"A Method to Estimate Exogenous Disturbances in Nonlinear Systems Based on Equivalent-Input-Disturbance Approach","authors":"Xiang Yin, Jinhua She, Zhentao Liu, Min Wu, Daiki Sato, K. Hirota","doi":"10.1109/INDIN41052.2019.8972265","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972265","url":null,"abstract":"A method to suppress exogenous disturbances in a nonlinear system is presented based on equivalent-input-disturbance (EID) approach. The nonlinear term is considered to be useful and should not be rejected in this paper. The method has two exceptional merits: it is a output-feedback control method, and the only restriction of the nonlinear term is used to ensure the stability of the NEID-based control system. Finally, the design procedure is illustrated by a numerical example.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125061402","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 : 2019-07-01DOI: 10.1109/INDIN41052.2019.8972272
Chandan Sharma, R. Sinha, P. Leitão
The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.
{"title":"IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases","authors":"Chandan Sharma, R. Sinha, P. Leitão","doi":"10.1109/INDIN41052.2019.8972272","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972272","url":null,"abstract":"The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, \"Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions\" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125079530","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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