{"id":103412,"date":"2024-02-20T13:24:21","date_gmt":"2024-02-20T12:24:21","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103412"},"modified":"2024-05-13T19:46:39","modified_gmt":"2024-05-13T17:46:39","slug":"maintenance","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/maintenance\/","title":{"rendered":"Maintenance"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Adaptive\n <\/picture>\n <\/div>\n
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\t\t

Adaptive In-Orbit Servicing of Altered Satellite Components<\/h4>\n
Adaptive gripper placement on altered components for servicing in-orbit satellites<\/div>
Justus Rein<\/a> \n \"ORCID<\/a>, Christian Plesker<\/a> \n \"ORCID<\/a>, Adrian Reuther<\/a> \n \"ORCID<\/a>, Hanyu Liu<\/a> \n \"ORCID<\/a>, Benjamin Schleich<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In-orbit servicing of satellites presents several challenges as the satellite hardware is exposed to external influences throughout its life cycle. These factors wear down the components and cause changes to their physical structure. In such cases, the limits of simple dis- and reassembly steps may be reached, as the gripping surfaces are no longer present or suitable. This paper proposes an approach of an adaptive grip position estimation in a CubeSat disassembly process. The relevant components are identified using CAD models and a 3D camera. The gripping positions are determined based on the geometry of the gripper and the point cloud of the component. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 10-21 | DOI 10.30844\/I4SE.25.6.10<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Bridging\n <\/picture>\n <\/div>\n
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Bridging Knowledge Gaps with GenAI in Industrial Maintenance<\/h4>\n
Specific needs and contextualized solutions<\/div>
Uta Wilkens<\/a> \n \"ORCID<\/a>, Julian Polte<\/a> \n \"ORCID<\/a>, Philipp Lelidis<\/a>, Eckart Uhlmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The paper specifies the genAI support needs for industrial maintenance against the background of a sociotechnical systems perspective. Emphasizing two needs, accessing implicit operator knowledge and prioritizing complex regulatory knowledge, a multi-layer architecture is outlined for an AI-based context-sensitive maintenance assistance system (MAS). The main purpose is to bridge knowledge gaps with genAI if human expertise and human implicit knowledge are not available and to cope with sub-process-specific challenges of multiple regulations. The MAS facilitates access to technical knowledge, distributes expertise, and shares implicit knowledge of experienced operators across different layers of information processing. The approach goes beyond standardization and has a high potential to enhance organizational as well as individual resilience. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 50-57 | DOI 10.30844\/I4SE.25.5.50<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Smart Workstation for Industrial Quality Control<\/h4>\n
Enhancing productivity through vision systems, real-time assistance, and Axiomatic Design<\/div>
Leonardo Venturoso<\/a> \n \"ORCID<\/a>, Simone Garbin<\/a> \n \"ORCID<\/a>, Dieter Steiner<\/a> \n \"ORCID<\/a>, Dominik T. Matt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system\u2019s practical benefits and its scalability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 128-134 | DOI 10.30844\/I4SE.25.5.124<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI-Supported\n <\/picture>\n <\/div>\n
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AI-Supported Personnel Planning in Industrial Maintenance<\/h4>\n
User-centered development and implementation in a pilot project<\/div>
Philipp Hein<\/a> \n \"ORCID<\/a>, Katharina Simon<\/a> \n \"ORCID<\/a>, Alexander K\u00f6gel<\/a>, Angelika C. Bullinger-Hoffmann<\/a> \n \"ORCID<\/a>, Thomas L\u00f6ffler<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Personnel deployment planning in industrial maintenance is a complex challenge, as dispatchers often have to match incomplete customer requests with the appropriate employee skills. An AI-based assistance system can help by automatically analyzing relevant data and providing well-founded suggestions for employee selection. This article describes the user-centered development and introduction of such a system as part of a pilot project at a medium-sized service provider. The user-centered design ensures that dispatchers retain their autonomy. Involving employees from the outset creates acceptance and promotes a deeper understanding of the system\u2019s advantages. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 14-20 | DOI 10.30844\/I4SE.25.5.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Collaborative\n <\/picture>\n <\/div>\n
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Collaborative Drone Inspection<\/h4>\n
A new approach to inspection work with AI support<\/div>
Till Becker<\/a>, Agron Neziraj<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Drone technology and the use of artificial intelligence (AI) offer promising advantages in various sectors, including in inspection. The use of innovative inspection technologies can make inspections more efficient overall. This research project examines various legal and economic aspects of AI-based autonomous drone inspections. It also develops a target process that represents the use of an AI-based drone inspection and controls the use of such inspection technology. In particular, this article focuses on a collaborative approach to this new inspection methodology. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 94-100<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Turning\n <\/picture>\n <\/div>\n
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Turning in Circles<\/h4>\n
Exploiting the potential of circular economy in wind turbine operations <\/div>
Sebastian Schlund<\/a> \n \"ORCID<\/a>, Stefanie Eisl<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The decarbonization of the energy sector is crucial for a climate-neutral EU, as a large proportion of greenhouse gas emissions come from energy use. Especially the wind energy sector, with its high material costs, faces major challenges. The rapid expansion of wind energy requires innovative solutions to establish sustainable End-of-Life (EoL) management practices. A digital decision-making framework for sustainable EoL strategies is therefore extremely useful. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 5 | Pages 90-98 | DOI 10.30844\/I4SE.24.5.90<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Building\n <\/picture>\n <\/div>\n
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Building Blocks for an Additive Manufacturing-Based Service Network<\/h4>\n
Britta Wortmann<\/a>, David Kiklhorn<\/a>, Andreas Witte<\/a>, Daniel Klima<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>The \u201cIT\u2019S DIGITIVE\u201d research project developed the prerequisites for collaborative and platform-supported processing of additive manufacturing-based services and thus important building blocks for an additive manufacturing-based service network. The focus was on intellectual property protection and the development of secure and trustworthy order fulfillment processes. Based on the identified inherent risks and threats in this distributed order processing, appropriate security countermeasures were developed using two use cases as examples and implemented as demonstrators. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 37 | 2021 | Edition 5 | Pages 57-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Maintenance\n <\/picture>\n <\/div>\n
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Maintenance 4.0<\/h4>\n
A concept for the representation of maintenance processes and the role of humans in the Industry 4.0<\/div>
Michael Kelker<\/a>, Roland Heidel<\/a>, Lennart Brumby<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>This article presents a concept for the visualization of maintenance processes in Industry 4.0 using the reference architecture model Industry 4.0, in short RAMI 4.0. The mirroring of maintenance processes is done by transferring relevant maintenance terms from various standards into maintenance assets and the corresponding management shells. Here, the role of the human being has to be considered and the dynamic behaviour of maintenance processes has to be mapped. In this respect, this concept presents different methods of visualizing these issues. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 37 | 2021 | Edition 3 | Pages 63-66<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Approach\n <\/picture>\n <\/div>\n
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\t\t

Approach to the Condition Description of Technical Components<\/h4>\n
Prediction of remaining useful life based on discretely recorded component states using mobile sensor technology<\/div>
Lukas Egbert<\/a> \n \"ORCID<\/a>, Anton Zitnikov<\/a> \n \"ORCID<\/a>, Thorsten Tietjen<\/a>, Klaus-Dieter Thoben<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 37 | 2021 | Edition 2 | Pages 35-38 | DOI 10.30844\/I40M_21-2_S35-38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n4<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103410,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103412","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103412","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103412"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103412\/revisions"}],"predecessor-version":[{"id":103414,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103412\/revisions\/103414"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103410"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}