Pub Date : 1900-01-01DOI: 10.5220/0011553500003329
N. Iftikhar, Adrian Dohot
: An asset failure is costly for the manufacturing industry as it causes unplanned downtime. Unplanned downtime halts production lines, and can lead to productivity loss. One of the widely used methods to reduce downtime is to make use of condition based maintenance. The goal of condition based maintenance is to monitor as well as detect present and/or upcoming asset failures and thus reduce unplanned downtime. A newly emerged phenomena is to monitor the asset condition at real-time. Thus, this paper presents the techniques to process data-in-motion in order to monitor the health and condition of industrial assets in real-time. The techniques presented in this paper require no historical and/or labeled data and work well on streaming data.
{"title":"Condition based Maintenance on Data Streams in Industry 4.0","authors":"N. Iftikhar, Adrian Dohot","doi":"10.5220/0011553500003329","DOIUrl":"https://doi.org/10.5220/0011553500003329","url":null,"abstract":": An asset failure is costly for the manufacturing industry as it causes unplanned downtime. Unplanned downtime halts production lines, and can lead to productivity loss. One of the widely used methods to reduce downtime is to make use of condition based maintenance. The goal of condition based maintenance is to monitor as well as detect present and/or upcoming asset failures and thus reduce unplanned downtime. A newly emerged phenomena is to monitor the asset condition at real-time. Thus, this paper presents the techniques to process data-in-motion in order to monitor the health and condition of industrial assets in real-time. The techniques presented in this paper require no historical and/or labeled data and work well on streaming data.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133484659","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 : 1900-01-01DOI: 10.5220/0010123200240031
J. Szpytko, Yorlandys Salgado Duarte
The subject of the paper is the exploitation efficiency system of overhead type cranes operating in critical systems, results implementation the control risk management and maintenance scheduling processes. The study case of the paper is a hot rolling mills system of a steel plant with critical overhead cranes operating with hazard conditions and continuous operation. The model output is an optimal overhead cranes maintenance scheduling distribution minimizing the production line risk stopped and the model input is a digital database structure with historical information related with the operation, maintenance, logistics and management process of the overhead cranes in the hot rolling mills plant. The transfer function is a stochastic non-linear optimization model with bounded constraint that assess a risk global-system indicator based on Monte Carlo simulations.
{"title":"Exploitation Efficiency System of Crane based on Risk Management","authors":"J. Szpytko, Yorlandys Salgado Duarte","doi":"10.5220/0010123200240031","DOIUrl":"https://doi.org/10.5220/0010123200240031","url":null,"abstract":"The subject of the paper is the exploitation efficiency system of overhead type cranes operating in critical systems, results implementation the control risk management and maintenance scheduling processes. The study case of the paper is a hot rolling mills system of a steel plant with critical overhead cranes operating with hazard conditions and continuous operation. The model output is an optimal overhead cranes maintenance scheduling distribution minimizing the production line risk stopped and the model input is a digital database structure with historical information related with the operation, maintenance, logistics and management process of the overhead cranes in the hot rolling mills plant. The transfer function is a stochastic non-linear optimization model with bounded constraint that assess a risk global-system indicator based on Monte Carlo simulations.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"30 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114002954","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 : 1900-01-01DOI: 10.5220/0010055700760086
J. Zeiler, Anja Mecklenburg, J. Fottner
Current research approaches in the field of logistics discuss the transformation of load carriers into smart objects. These so-called cyber physical systems collect data, aiming for process optimisation and increased transparency. Though special load carriers are commonly used in the automotive industry and have great potential in terms of digitalisation, they are mostly neglected. Understocking and overstocking, as well as production stops due to missing or damaged containers can result from insufficient transparency in supply chains. This paper presents the benefit and usability evaluation of a service system with smart and modular special load carriers, which aims to counteract this lack of transparency by providing databased services. In the therefore concluded web-based survey, experts evaluated the identified benefits in terms of impacts on the process, the customer and the environment. The presented results show that the benefits generated by the service system are suitable for optimising the conditions for the logistic process, the customer, the environment and the transparency within the supply chain. Although the already implemented functionalities of the service system are still limited in usability, the theoretical concepts and its functionalities have great potential in terms of future applications.
{"title":"Evaluation of a Service System for Smart and Modular Special Load Carriers within Industry 4.0","authors":"J. Zeiler, Anja Mecklenburg, J. Fottner","doi":"10.5220/0010055700760086","DOIUrl":"https://doi.org/10.5220/0010055700760086","url":null,"abstract":"Current research approaches in the field of logistics discuss the transformation of load carriers into smart objects. These so-called cyber physical systems collect data, aiming for process optimisation and increased transparency. Though special load carriers are commonly used in the automotive industry and have great potential in terms of digitalisation, they are mostly neglected. Understocking and overstocking, as well as production stops due to missing or damaged containers can result from insufficient transparency in supply chains. This paper presents the benefit and usability evaluation of a service system with smart and modular special load carriers, which aims to counteract this lack of transparency by providing databased services. In the therefore concluded web-based survey, experts evaluated the identified benefits in terms of impacts on the process, the customer and the environment. The presented results show that the benefits generated by the service system are suitable for optimising the conditions for the logistic process, the customer, the environment and the transparency within the supply chain. Although the already implemented functionalities of the service system are still limited in usability, the theoretical concepts and its functionalities have great potential in terms of future applications.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114526190","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 : 1900-01-01DOI: 10.5220/0011589600003329
Leonhard Faubel, Klaus Schmid, Holger Eichelberger
: An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.
{"title":"Is MLOps different in Industry 4.0? General and Specific Challenges","authors":"Leonhard Faubel, Klaus Schmid, Holger Eichelberger","doi":"10.5220/0011589600003329","DOIUrl":"https://doi.org/10.5220/0011589600003329","url":null,"abstract":": An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353490","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 : 1900-01-01DOI: 10.5220/0011536700003329
Y. Eslami, Sahand Ashouri, Chiara Franciosi, Mario Lezoche
{"title":"Knowledge Extraction in Cyber-Physical Systems Meta-models: A Formal Concept Analysis Application","authors":"Y. Eslami, Sahand Ashouri, Chiara Franciosi, Mario Lezoche","doi":"10.5220/0011536700003329","DOIUrl":"https://doi.org/10.5220/0011536700003329","url":null,"abstract":"","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116794572","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 : 1900-01-01DOI: 10.5220/0011258700003329
John O'Sullivan, B. O'Flaherty, T. O'Kane
: This paper is a literature review to determine if industrial applications are appropriately represented in information systems (IS) scholarship. The field of Industry 4.0 was used as a representative sample of industrial information systems and the Association for Information Systems (AIS) Senior Scholars’ Basket of Journals was used as a representative, albeit highly ranked, sample of IS literature. Keywords representing the eleven recognised technologies of Industry 4.0 were chosen and used to search the eight IS journals over a time period corresponding with the lifecycle of Industry 4.0. This resulted in 1305 papers being discovered. After calibrating the search terms, a second search yielded 770 papers. These papers were screened for relevance to Industry 4.0 and for use of a manufacturing application. The resulting 20 papers were queried in detail to establish the concepts used and a concept centric matrix was produced. The analysis shows that industrial information applications are rarely used to undertake IS research in the academic field. The dominant concept revealed was digital transformation resulting in changes to business processes. The contribution to the literature is to highlight that substantial research studies can be conducted in the industrial manufacturing arena, but very few have been conducted in the last decade. Therefore, it is an area worth exploring for future IS research.
{"title":"Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0","authors":"John O'Sullivan, B. O'Flaherty, T. O'Kane","doi":"10.5220/0011258700003329","DOIUrl":"https://doi.org/10.5220/0011258700003329","url":null,"abstract":": This paper is a literature review to determine if industrial applications are appropriately represented in information systems (IS) scholarship. The field of Industry 4.0 was used as a representative sample of industrial information systems and the Association for Information Systems (AIS) Senior Scholars’ Basket of Journals was used as a representative, albeit highly ranked, sample of IS literature. Keywords representing the eleven recognised technologies of Industry 4.0 were chosen and used to search the eight IS journals over a time period corresponding with the lifecycle of Industry 4.0. This resulted in 1305 papers being discovered. After calibrating the search terms, a second search yielded 770 papers. These papers were screened for relevance to Industry 4.0 and for use of a manufacturing application. The resulting 20 papers were queried in detail to establish the concepts used and a concept centric matrix was produced. The analysis shows that industrial information applications are rarely used to undertake IS research in the academic field. The dominant concept revealed was digital transformation resulting in changes to business processes. The contribution to the literature is to highlight that substantial research studies can be conducted in the industrial manufacturing arena, but very few have been conducted in the last decade. Therefore, it is an area worth exploring for future IS research.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123836136","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 : 1900-01-01DOI: 10.5220/0010655000003062
Alessandro Rizzuto, David Govi, F. Schipani, Alessandro Lazzeri
: This project is presented as a real case-study based on machine learning and deep learning algorithms which are compared for a clearer understanding of which procedure is more suitable to industrial drilling.The predic-tions are obtained by using algorithms with a pre-processed dataset which was made available by the industry. The losses of each algorithm together with the SHAP values are reported, in order to understand which features most influenced the final prediction.
{"title":"Lead Time Estimation of a Drilling Factory with Machine and Deep Learning Algorithms: A Case Study","authors":"Alessandro Rizzuto, David Govi, F. Schipani, Alessandro Lazzeri","doi":"10.5220/0010655000003062","DOIUrl":"https://doi.org/10.5220/0010655000003062","url":null,"abstract":": This project is presented as a real case-study based on machine learning and deep learning algorithms which are compared for a clearer understanding of which procedure is more suitable to industrial drilling.The predic-tions are obtained by using algorithms with a pre-processed dataset which was made available by the industry. The losses of each algorithm together with the SHAP values are reported, in order to understand which features most influenced the final prediction.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132329800","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 : 1900-01-01DOI: 10.5220/0010713600003062
W. Cook, Haley Felberg, Natalie Palos, J. Yeh
: In this paper, we discuss our construction of a natural gas monitoring system that utilizes a network of nodes that communicate with each other using LoRa modulation techniques. After the devastating gas leak in 2015 at the Aliso Canyon Natural Gas Storage Facility in Los Angeles county, in which a total of 104,400 tonnes of methane and ethane gas was released into the atmosphere, it became apparent that gas storage facilities and pipelines are in need of more efficient gas leak observation and monitoring methods. Our solution involves constructing nodes from a LoRa32 microcontroller, MQ-4 gas sensor, solar panel, and a 3.7V lithium battery. The nodes will be configured in a daisy-chain topology that can be positioned along any pipeline or gas storage facility. The daisy-chain topology will allow data to be sent along the chain to a data collection node and subsequently stored in the cloud hosted Firebase database. It is also anticipated that this monitoring system will be surveyed using an intuitive mobile application for iOS and Android devices.
{"title":"IoT Natural Gas Pipeline Monitoring System","authors":"W. Cook, Haley Felberg, Natalie Palos, J. Yeh","doi":"10.5220/0010713600003062","DOIUrl":"https://doi.org/10.5220/0010713600003062","url":null,"abstract":": In this paper, we discuss our construction of a natural gas monitoring system that utilizes a network of nodes that communicate with each other using LoRa modulation techniques. After the devastating gas leak in 2015 at the Aliso Canyon Natural Gas Storage Facility in Los Angeles county, in which a total of 104,400 tonnes of methane and ethane gas was released into the atmosphere, it became apparent that gas storage facilities and pipelines are in need of more efficient gas leak observation and monitoring methods. Our solution involves constructing nodes from a LoRa32 microcontroller, MQ-4 gas sensor, solar panel, and a 3.7V lithium battery. The nodes will be configured in a daisy-chain topology that can be positioned along any pipeline or gas storage facility. The daisy-chain topology will allow data to be sent along the chain to a data collection node and subsequently stored in the cloud hosted Firebase database. It is also anticipated that this monitoring system will be surveyed using an intuitive mobile application for iOS and Android devices.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116985135","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 : 1900-01-01DOI: 10.1007/978-3-031-37228-5_5
S. Schäfer, Dirk Schöttke, T. Kämpfe, O. Lachmann, Aaron Zielstorff
{"title":"Synchronizing Devices Using Asset Administration Shells","authors":"S. Schäfer, Dirk Schöttke, T. Kämpfe, O. Lachmann, Aaron Zielstorff","doi":"10.1007/978-3-031-37228-5_5","DOIUrl":"https://doi.org/10.1007/978-3-031-37228-5_5","url":null,"abstract":"","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606843","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 : 1900-01-01DOI: 10.5220/0010174901240131
M. Butturi, F. Lolli, Chiara Menini
Within the paradigm of Industry 4.0, digital reconfigurable manufacturing and assembly systems can rapidly adapt to dynamic market demand, modifying their capacity and functionality. In manual or hybrid reconfigurable assembly systems, the rapid and frequent variations in the performed tasks subject workers to a significant cognitive load, making relevant the learning-forgetting phenomenon. In fact, the operators carry out the assigned activities for a short time before a reconfiguration of the system takes place, assigning them tasks often different from those just performed. This paper aims at investigating how the tasks’ execution time varies for operators working along a reconfigurable assembly line, depending on the learning forgetting effect. We applied a Kottas-Lau algorithm, considering the expected execution times updated according to a learning-forgetting curve. A numerical example, considering with five successive reconfigurations, allows to analyse the expected execution time trend for each operator-task pair and the variation in costs obtained as the operators learning rate and the variability of the operations change.
{"title":"Balancing of Manual Reconfigurable Assembly Systems with Learning and Forgetting Effects","authors":"M. Butturi, F. Lolli, Chiara Menini","doi":"10.5220/0010174901240131","DOIUrl":"https://doi.org/10.5220/0010174901240131","url":null,"abstract":"Within the paradigm of Industry 4.0, digital reconfigurable manufacturing and assembly systems can rapidly adapt to dynamic market demand, modifying their capacity and functionality. In manual or hybrid reconfigurable assembly systems, the rapid and frequent variations in the performed tasks subject workers to a significant cognitive load, making relevant the learning-forgetting phenomenon. In fact, the operators carry out the assigned activities for a short time before a reconfiguration of the system takes place, assigning them tasks often different from those just performed. This paper aims at investigating how the tasks’ execution time varies for operators working along a reconfigurable assembly line, depending on the learning forgetting effect. We applied a Kottas-Lau algorithm, considering the expected execution times updated according to a learning-forgetting curve. A numerical example, considering with five successive reconfigurations, allows to analyse the expected execution time trend for each operator-task pair and the variation in costs obtained as the operators learning rate and the variability of the operations change.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123186190","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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