Pub Date : 2020-11-01DOI: 10.1109/INDIN.2017.8104948
C. Wu, Hongxu Zhu, L. Lai, Anna S. F. Chang, Fengjun Li, K. Tsang, R. Kalawsky
Water management is an important issue in economics and environment. Recently, amount of water control system has been proposed and developed. For the type of intelligent water control, the related parameters will be the input of the control system. Hence, there is a need of developing a scalable, flexible and reliable sensor network for related parameters monitoring. To install and replace water sensors in building networks, wireless connection will be the first priority. However, improper time synchronization in the network will cause packet loss and long latency which degrades the network performance. In this paper, time-synchronized ZigBee building network (TS-ZBN) is proposed for water management. The node-to-node time synchronization is proposed. The concept is to calculate the clock difference by studying the propagation delay model. The simulation result shows that the mean synchronization error and variance are low.
{"title":"A time-synchronized ZigBee building network for smart water management","authors":"C. Wu, Hongxu Zhu, L. Lai, Anna S. F. Chang, Fengjun Li, K. Tsang, R. Kalawsky","doi":"10.1109/INDIN.2017.8104948","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104948","url":null,"abstract":"Water management is an important issue in economics and environment. Recently, amount of water control system has been proposed and developed. For the type of intelligent water control, the related parameters will be the input of the control system. Hence, there is a need of developing a scalable, flexible and reliable sensor network for related parameters monitoring. To install and replace water sensors in building networks, wireless connection will be the first priority. However, improper time synchronization in the network will cause packet loss and long latency which degrades the network performance. In this paper, time-synchronized ZigBee building network (TS-ZBN) is proposed for water management. The node-to-node time synchronization is proposed. The concept is to calculate the clock difference by studying the propagation delay model. The simulation result shows that the mean synchronization error and variance are low.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"108 1","pages":"1219-1222"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76269711","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104818
Tatsuki Miura, J. Wijekoon, Shanaka Prageeth, H. Nishi
The development of smart communities has diversified not only service execution platforms but also the resolvers of multiple service requirements, each of which has different requirements in terms of processing delay, anonymity, computational cost, the amount of data at a given level of granularity, etc. To meet these requirements, an infrastructure that easily performs service migration and provides services with the correct processing nodes using IP-independent distributed processing methods such as Authorized Stream Contents Analysis (ASCA) is becoming a pressing need of smart communities. ASCA is an advanced method of analyzing packet streams and filtering necessary packet streams according to the marker tags in the contents of the streams under the Opt-In manner. Moreover, smart communities require that every service be able to perform ASCA and gather necessary data because of the diversified nature of the services. Consequently, in this paper, we have implemented a service infrastructure using a Docker container that facilitates service migration and provides services with a common Application Programming Interface (API) using ASCA. The API provides a process throughput of over 60 Gbps on a Docker container using Zero-Copy mechanism.
{"title":"Novel infrastructure with common API using docker for scaling the degree of platforms for smart community services","authors":"Tatsuki Miura, J. Wijekoon, Shanaka Prageeth, H. Nishi","doi":"10.1109/INDIN.2017.8104818","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104818","url":null,"abstract":"The development of smart communities has diversified not only service execution platforms but also the resolvers of multiple service requirements, each of which has different requirements in terms of processing delay, anonymity, computational cost, the amount of data at a given level of granularity, etc. To meet these requirements, an infrastructure that easily performs service migration and provides services with the correct processing nodes using IP-independent distributed processing methods such as Authorized Stream Contents Analysis (ASCA) is becoming a pressing need of smart communities. ASCA is an advanced method of analyzing packet streams and filtering necessary packet streams according to the marker tags in the contents of the streams under the Opt-In manner. Moreover, smart communities require that every service be able to perform ASCA and gather necessary data because of the diversified nature of the services. Consequently, in this paper, we have implemented a service infrastructure using a Docker container that facilitates service migration and provides services with a common Application Programming Interface (API) using ASCA. The API provides a process throughput of over 60 Gbps on a Docker container using Zero-Copy mechanism.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"25 1","pages":"474-479"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75999218","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104743
Charles Steinmetz, Greyce N. Schroeder, A. Roque, C. Pereira, Carolin Wagner, Philipp Saalmann, B. Hellingrath
The Internet of Things concept is increasingly being used in projects from different areas. Often, projects encompass applications from different domains that need to share information for a common purpose. These types of systems can become complex because they involve different domains and it can be difficult to share information without misinterpreting certain information. Ontologies can be a way to describe information to be shared between different domains. The present work aims to propose a tool that facilitates the creation of Internet of Things systems using ontologies and through this model generate code automatically to pre-automate the integration of devices. A tool has been developed that facilitates the extraction of information from the ontology and it has been validated in the case study.
{"title":"Ontology-driven IoT code generation for FIWARE","authors":"Charles Steinmetz, Greyce N. Schroeder, A. Roque, C. Pereira, Carolin Wagner, Philipp Saalmann, B. Hellingrath","doi":"10.1109/INDIN.2017.8104743","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104743","url":null,"abstract":"The Internet of Things concept is increasingly being used in projects from different areas. Often, projects encompass applications from different domains that need to share information for a common purpose. These types of systems can become complex because they involve different domains and it can be difficult to share information without misinterpreting certain information. Ontologies can be a way to describe information to be shared between different domains. The present work aims to propose a tool that facilitates the creation of Internet of Things systems using ontologies and through this model generate code automatically to pre-automate the integration of devices. A tool has been developed that facilitates the extraction of information from the ontology and it has been validated in the case study.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"58 1","pages":"38-43"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84704794","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104838
Hermann Haskamp, Michaelene A Meyer, Romina Mollmann, Florian Orth, A. Colombo
A standardized Communication / Information Protocol is necessary for implementing the 4 upper digitalization layers of Industrie 4.0-compliant solutions. OPC UA is one of the leading and preferred technology. Currently, there are many different OPC UA implementations available on the market. This paper gives an overview of existing solutions and shows the results of an initial benchmarking based on a set of evaluation criteria defined by the authors. Moreover, the manuscript identifies both free-of-charge and commercial solutions that are more adequate for implementing industrial applications.
{"title":"Benchmarking of existing OPC UA implementations for Industrie 4.0-compliant digitalization solutions","authors":"Hermann Haskamp, Michaelene A Meyer, Romina Mollmann, Florian Orth, A. Colombo","doi":"10.1109/INDIN.2017.8104838","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104838","url":null,"abstract":"A standardized Communication / Information Protocol is necessary for implementing the 4 upper digitalization layers of Industrie 4.0-compliant solutions. OPC UA is one of the leading and preferred technology. Currently, there are many different OPC UA implementations available on the market. This paper gives an overview of existing solutions and shows the results of an initial benchmarking based on a set of evaluation criteria defined by the authors. Moreover, the manuscript identifies both free-of-charge and commercial solutions that are more adequate for implementing industrial applications.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"3 1","pages":"589-594"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85023764","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104881
Barış Gün Sürmeli, Feyza Eksen, Bilal Dinc, P. Schüller, M. Tümer
Sequential data generated from various sources in a multi-mode industrial production system provides valuable information on the current mode of the system and enables one to build a model for each individual operating mode. Using these models in a multi-mode system, one may distinguish modes of the system and, furthermore, detect whether the current mode is a (normal or faulty) mode known from historical data, or a new mode. In this work, we model each individual mode by a probabilistic suffix tree (PST) used to implement variable order Markov models (VOMMs) and propose a novel unsupervised PST matching algorithm that compares the tree models by a matching cost once they are constructed. The matching cost we define comprises of a subsequence dissimilarity cost and a probability cost. Our tree matching method enables to compare two PSTs in linear time by one concurrent top-down pass. We use this matching cost as a similarity measure for k-medoid clustering and cluster PSTs obtained from system modes according to their matching costs. The overall approach yields promising results for unsupervised identification of modes on data obtained from of a physical factory demonstrator. Notably we can distinguish modes on two levels of granularity, both corresponding to human expert labels, with a RAND score of up to 73 % compared to a baseline of at most 42 %.
{"title":"Unsupervised mode detection in cyber-physical systems using variable order Markov models","authors":"Barış Gün Sürmeli, Feyza Eksen, Bilal Dinc, P. Schüller, M. Tümer","doi":"10.1109/INDIN.2017.8104881","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104881","url":null,"abstract":"Sequential data generated from various sources in a multi-mode industrial production system provides valuable information on the current mode of the system and enables one to build a model for each individual operating mode. Using these models in a multi-mode system, one may distinguish modes of the system and, furthermore, detect whether the current mode is a (normal or faulty) mode known from historical data, or a new mode. In this work, we model each individual mode by a probabilistic suffix tree (PST) used to implement variable order Markov models (VOMMs) and propose a novel unsupervised PST matching algorithm that compares the tree models by a matching cost once they are constructed. The matching cost we define comprises of a subsequence dissimilarity cost and a probability cost. Our tree matching method enables to compare two PSTs in linear time by one concurrent top-down pass. We use this matching cost as a similarity measure for k-medoid clustering and cluster PSTs obtained from system modes according to their matching costs. The overall approach yields promising results for unsupervised identification of modes on data obtained from of a physical factory demonstrator. Notably we can distinguish modes on two levels of granularity, both corresponding to human expert labels, with a RAND score of up to 73 % compared to a baseline of at most 42 %.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"42 1","pages":"841-846"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79068753","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104749
Dirk Jacobsen, P. Ott
Cloud computing offers the opportunity to minimize the evaluation time of complex algorithms — e.g. needed for computational imaging — by horizontal scaling of the available computing resources. By this way, new image analyzing algorithms can be employed in weak real-time conditions, like inline quality analysis in production with time stamps in the order of several tens of seconds. The cloud offers a platform to merge sensor data of all production processes to analyze quality data comprehensively, e.g. for methods like predictive maintenance. Typically, cloud environments are applied for the Internet of things (IoT) or Big Data analysis. But IoT-applications usually generate very small data packages (like sensor values with a size much less than 1 megabyte), while BigData applications deal with very high data volume (terra-or petabyte). Image processing requires an environment, which is optimized for medium size data streaming, composed of images with a size in the lower megabyte range. In this paper, a sensor-to-cloud architecture as a platform for image processing is described. This approach is upward compatible, because it is not necessary to change the sensor hardware, e.g. if algorithms with considerable higher computing complexity are desired (like for a smart camera), so algorithms can be exchanged in the cloud without interrupting the production process.
{"title":"Cloud architecture for industrial image processing: Platform for realtime inline quality assurance","authors":"Dirk Jacobsen, P. Ott","doi":"10.1109/INDIN.2017.8104749","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104749","url":null,"abstract":"Cloud computing offers the opportunity to minimize the evaluation time of complex algorithms — e.g. needed for computational imaging — by horizontal scaling of the available computing resources. By this way, new image analyzing algorithms can be employed in weak real-time conditions, like inline quality analysis in production with time stamps in the order of several tens of seconds. The cloud offers a platform to merge sensor data of all production processes to analyze quality data comprehensively, e.g. for methods like predictive maintenance. Typically, cloud environments are applied for the Internet of things (IoT) or Big Data analysis. But IoT-applications usually generate very small data packages (like sensor values with a size much less than 1 megabyte), while BigData applications deal with very high data volume (terra-or petabyte). Image processing requires an environment, which is optimized for medium size data streaming, composed of images with a size in the lower megabyte range. In this paper, a sensor-to-cloud architecture as a platform for image processing is described. This approach is upward compatible, because it is not necessary to change the sensor hardware, e.g. if algorithms with considerable higher computing complexity are desired (like for a smart camera), so algorithms can be exchanged in the cloud without interrupting the production process.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"72-74"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77309759","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104742
M. Moghaddam, C. Robert Kenley, Julia M. Colby, Marissa N. Cadavid Berns, R. Rausch, J. Markham, W. Skeffington, J. Garrity, A. Chaturvedi, A. Deshmukh
Industry 4.0 is opening new avenues for reconfigurable and information-centric integration of enterprise functions and control systems. Most of the current approaches (e.g., ISA-95) view enterprise architectures in a pre-defined, monolithic, and hierarchical sense. To enable more innovative, personalized, and efficient manufacturing processes, however, such ‘tree-like’ architectures must turn into decentralized and cyber-physical networks of ‘things’ and ‘services’. This article investigates the emerging Industry 4.0 paradigms and architectures (e.g., IIRA, RAMI4.0), their commonalities, limitations, and evolution towards modular and service-oriented architectures (eg., ISO/IEC 18384:2016). The goal is to identify and analyze the existing technical and technological gaps, and provide recommendations for developing new reference models/architectures for next-generation enterprises.
工业4.0为企业功能和控制系统的可重构和以信息为中心的集成开辟了新的途径。大多数当前的方法(例如，ISA-95)以一种预定义的、整体的和分层的方式来看待企业架构。然而，为了实现更加创新、个性化和高效的制造流程，这种“树状”架构必须转变为分散的“物”和“服务”网络。本文研究了新兴的工业4.0范式和体系结构(例如，IIRA、RAMI4.0)、它们的共性、局限性以及向模块化和面向服务的体系结构(例如:， iso / iec 18384:2016)。目标是识别和分析现有的技术和技术差距，并为为下一代企业开发新的参考模型/体系结构提供建议。
{"title":"Next-generation enterprise architectures: Common vernacular and evolution towards service-orientation","authors":"M. Moghaddam, C. Robert Kenley, Julia M. Colby, Marissa N. Cadavid Berns, R. Rausch, J. Markham, W. Skeffington, J. Garrity, A. Chaturvedi, A. Deshmukh","doi":"10.1109/INDIN.2017.8104742","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104742","url":null,"abstract":"Industry 4.0 is opening new avenues for reconfigurable and information-centric integration of enterprise functions and control systems. Most of the current approaches (e.g., ISA-95) view enterprise architectures in a pre-defined, monolithic, and hierarchical sense. To enable more innovative, personalized, and efficient manufacturing processes, however, such ‘tree-like’ architectures must turn into decentralized and cyber-physical networks of ‘things’ and ‘services’. This article investigates the emerging Industry 4.0 paradigms and architectures (e.g., IIRA, RAMI4.0), their commonalities, limitations, and evolution towards modular and service-oriented architectures (eg., ISO/IEC 18384:2016). The goal is to identify and analyze the existing technical and technological gaps, and provide recommendations for developing new reference models/architectures for next-generation enterprises.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"39 1","pages":"32-37"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86993329","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104751
M. Müller, Elmar Wings, L. Bergmann
Cyber-physical systems (CPS) are key enabling technologies for the fourth industrial revolution, referred to as Industrie 4.0 or Industry 4.0. The Reference Architecture Model Industrie 4.0 (RAMI4.0) has recently been standardized and OPC Unified Architecture (OPC UA) is listed as the sole recommendation for implementation of a communication layer. Many automation and control systems offer already implementations of OPC UA but no satisfying implementation of OPC UA was found for Arduino, a popular platform for engineering physical computing systems. This paper presents open source integration and application of a customizable OPC UA server on an Arduino Yun board using open62541, an open source and free implementation of OPC UA. The Arduino board discussed in this paper offers hot-end closed-loop temperature control for a 3D printer but the temperature set value and control parameters can be manipulated and requested via OPC UA using OPC UA clients. The application is verified using Prosys OPC UA Client and UaExpert. The results of our research can be used for developing open source cyber-physical systems without specialized knowledge in microcontroller programming, bringing Industry 4.0 applications into classrooms without effort.
{"title":"Developing open source cyber-physical systems for service-oriented architectures using OPC UA","authors":"M. Müller, Elmar Wings, L. Bergmann","doi":"10.1109/INDIN.2017.8104751","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104751","url":null,"abstract":"Cyber-physical systems (CPS) are key enabling technologies for the fourth industrial revolution, referred to as Industrie 4.0 or Industry 4.0. The Reference Architecture Model Industrie 4.0 (RAMI4.0) has recently been standardized and OPC Unified Architecture (OPC UA) is listed as the sole recommendation for implementation of a communication layer. Many automation and control systems offer already implementations of OPC UA but no satisfying implementation of OPC UA was found for Arduino, a popular platform for engineering physical computing systems. This paper presents open source integration and application of a customizable OPC UA server on an Arduino Yun board using open62541, an open source and free implementation of OPC UA. The Arduino board discussed in this paper offers hot-end closed-loop temperature control for a 3D printer but the temperature set value and control parameters can be manipulated and requested via OPC UA using OPC UA clients. The application is verified using Prosys OPC UA Client and UaExpert. The results of our research can be used for developing open source cyber-physical systems without specialized knowledge in microcontroller programming, bringing Industry 4.0 applications into classrooms without effort.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"63 1","pages":"83-88"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84329491","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104811
Y. Bolea, A. Grau-Saldes, H. Martínez
Pollution in coastal areas is very hazardous for population. The problem is even higher when this pollution occurs in bathing areas such as beaches in populated areas. In this work we present a new automatic control application for wastewater plants when pollution is caused by those plants because there is an excess of water to be treated and it is thrown to the sea with high levels of biological pollutants (bacteria…). Those problems can be aggravated if climatic conditions pull to the coastal area the untreated water without the cleaning action of sea water. The experimentation is done with real data and real conditions in Barcelona area, at Mediterranean sea, with the urban wastewater plant of Besòs.
{"title":"Computerized control strategy to prevent wastewater plants pollution","authors":"Y. Bolea, A. Grau-Saldes, H. Martínez","doi":"10.1109/INDIN.2017.8104811","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104811","url":null,"abstract":"Pollution in coastal areas is very hazardous for population. The problem is even higher when this pollution occurs in bathing areas such as beaches in populated areas. In this work we present a new automatic control application for wastewater plants when pollution is caused by those plants because there is an excess of water to be treated and it is thrown to the sea with high levels of biological pollutants (bacteria…). Those problems can be aggravated if climatic conditions pull to the coastal area the untreated water without the cleaning action of sea water. The experimentation is done with real data and real conditions in Barcelona area, at Mediterranean sea, with the urban wastewater plant of Besòs.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"11 1","pages":"437-438"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86252834","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 : 2017-07-24DOI: 10.1109/INDIN.2017.8104778
David Cemernek, H. Gursch, Roman Kern
The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.
{"title":"Big data as a promoter of industry 4.0: Lessons of the semiconductor industry","authors":"David Cemernek, H. Gursch, Roman Kern","doi":"10.1109/INDIN.2017.8104778","DOIUrl":"https://doi.org/10.1109/INDIN.2017.8104778","url":null,"abstract":"The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.","PeriodicalId":6595,"journal":{"name":"2017 IEEE 15th International Conference on Industrial Informatics (INDIN)","volume":"34 1","pages":"239-244"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86434496","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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