Maintenance 4.0 Technologies - New Opportunities for Sustainability Driven Maintenance

• Autorzy: Małgorzata Jasiulewicz-Kaczmarek , Stanisław Legutko , Piotr Kluk
• Języki publikacji: EN

Abstrakty

EN

Digitalization and sustainability are important topics for manufacturing industries as theyare affecting all parts of the production chain. Various initiatives and approaches are setup to help companies adopt the principles of the fourth industrial revolution with respectsustainability. Within these actions the use of modern maintenance approaches such asMaintenance 4.0 is highlighted as one of the prevailing smart & sustainable manufacturingtopics. The goal of this paper is to describe the latest trends within the area of maintenancemanagement from the perspective of the challenges of the fourth industrial revolution andthe economic, environmental and social challenges of sustainable development. In this work,intelligent and sustainable maintenance was considered in three perspectives. The first per-spective is the historical perspective, in relation to which evolution has been presented in theapproach to maintenance in accordance with the development of production engineering. Thenext perspective is the development perspective, which presents historical perspectives onmaintenance data and data-driven maintenance technology. The third perspective, presentsmaintenance in the context of the dimensions of sustainable development and potential opportunities for including data-driven maintenance technology in the implementation of theeconomic, environmental and social challenges of sustainable production.(original abstract)

Słowa kluczowe

EN

Maintenance; Industry 4.0; Data processing

PL

Obsługa eksploatacyjna; Przemysł 4.0; Przetwarzanie danych

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2020
• Tom: 11
• Numer: nr 2
• Strony: 74--87

Twórcy

autor

Małgorzata Jasiulewicz-Kaczmarek

• Poznan University of Technology, Poznan, Poland

autor

Stanisław Legutko

• Poznan University of Technology, Poznan, Poland

autor

Piotr Kluk

• Zelka Sp. z o.o., Psary Małe, Poland

Bibliografia

• Buchi G., Cugno M., Castagnoli R., Smart factory performance and Industry 4.0, Technological Forecasting & Social Change, 150 119790, 2020.
• Krdzalić A., Hodzić L., Sustainable engineering challenges towards Industry 4.0: A comprehensive review, Sustainable Engineering and Innovation, 1, 1, 1-23, 2019.
• Saucedo-Martinez J.A. et al., Industry 4.0 framework for management and operations: a review, Journal of Ambient Intelligence and Humanized Computing, 9, 789-801, 2018.
• Sanna A. et al., Using Hand-Held Devices To Support Augmented Reality-Based Maintenance And Assembly Tasks, IEEE International Conference on Consumer Electronics (ICCE), 178-179, 2015.
• Alcacer V., Cruz-Machado V., Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems, Engineering Science and Technology, an International Journal, 22, 3, 899919, 2019.
• Peruzzini M., Grandi F., Pellicciari M., Benchmarking of Tools for User Experience Analysis in Indus- try 4.0, Procedia Manufacturing, 11, 806-813, 2017.
• Ibarra D., Ganzaraina J., Igartua J.I., Business model innovation through Industry 4.0: A review, Procedia Manufacturing, 22, 4-10, 2018.
• Leyh C., Martin S., Schaffer T., Industry 4.0 and Lean Production - A Matching Relationship? An analysis of selected Industry 4.0 models, ACSIS 2017, 11, 989-993, 2017.
• Wang S. et al., Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination, Computer Networks, 101, 158-68, 2016.
• Bonvoisin J., Stark R., Seliger G., Field of Research in Sustainable Manufacturing, [in:] R. Stark et al. [Eds], Sustainable Manufacturing, Sustainable Production, Life Cycle Engineering and Management, 3-20, 2017.
• Machado C.G., Winroth M.P., da Silva E.H.D.R., Sustainable manufacturing in Industry 4.0: an emerging research agenda, International Journal of Production Research 2019.
• Franciosi C. et al., Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Re- view, IFAC PapersOnLine, 51-11, 903-908, 2018.
• Jasiulewicz-Kaczmarek M., Gola A., Maintenance 4.0 Technologies for Sustainable Manufacturing - an Overview, IFAC PapersOnLine, 52-10, 91-96, 2019.
• Jardine A.K.S., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical System and Signal Process, 20, 1483-1510, 2006.
• Passath T., Mertens K., Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool, IFAC PapersOnLine, 52-10, 364-369, 2019.
• Fumagalli L. et al., A smart maintenance tool for a safe electric arc furnace, IFAC-PapersOnLine, 49, 3, 19-24, 2016.
• Kans M., Galar D., The Impact of Maintenance 4.0 and Big Data Analytics within Strategic Asset Management, [in:] Galar D., Seneviratne D. [Eds], MP- MM 2016, conference proceedings, 96-103, 2017.
• Glazer O., Is there a shortcut to Industrial Analytics/Maintenance 4.0 implementation?, 2019, https://www.presenso.com/blog/maintenance4dep- loymentshortcuts.
• Haarman M., Predictive Maintenance 4.0, Main-novation 2017 https://www.mainnovation.com/nl/ events/vdm-xl-value-driven-maintenance-asset-ma- nagement-3/.
• Kinz A., Bernerstaetter R., Biedermann H., Lean Smart Maintenance -Efficient and Effective Asset Management for Smart Factories, MOTSP 2016 - 8th International Scientific Conference, 2016.
• Kumar U., Galar D., Maintenance in the Era of In- dustry 4.0 : Issues and Challenges, [in:] Quality, IT and Business Operations: Modeling and Optimization, 231-250, 2018.
• Bokrantz J. et al., Smart Maintenance: an empirically grounded conceptualization, International Journal of Production Economics, 2019.
• Jimenez-Cortadi A. et al., Predictive Maintenance on the Machining Process and Machine Tool, Applied Sciences, 10, 1, 224, 2020.
• Ganga D., Ramachandran V., IoT-Based Vibration Analytics of Electrical Machines, Internet of Things Journal IEEE, 5, 6, 4538-4549, 2018.
• Catenazzo D., O'Flynn B., Walsh M., On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0, ICST, 256-262, 2018.
• Chen J., Zhang R., Wu D., Equipment Maintenance Business Model Innovation for Sustainable Competitive Advantage in the Digitalization Context: Connotation, Types, and Measuring, Sustainability, 10, 3970. 2018.
• Grubic T., Remote monitoring technology and servitization: Exploring the relationship, Computers in Industry, 100, 148-158, 2018.
• Ansari F., Glawar R., Nemeth T., PriMa: a prescriptive maintenance model for cyber-physical production systems, International Journal of Computer Integrated Manufacturing, 32, 4-5, 482-503, 2019.
• Matyas K. et al., A procedural approach for real- izing prescriptive maintenance planning in manufacturing industries, CIRP Annals - Manufacturing Technology, 66, 461-464, 2017.
• Antomarioni S. et al., Defining a data-driven maintenance policy: an application to an oil refinery plant, Int. J. Qual. Reliab. Manag., 36, 1, 77-97, 2019.
• Sai V.C., Shcherbakov M.V., Tran V.P., Data- driven framework for predictive maintenance in Industry 4.0 concept, Communications in Computer and Information Science, 1083, 344-358, 2019.
• Zhang W., Yang D., Wang H., Data-driven methods for predictive maintenance of industrial equipment: a survey, IEEE Systems Journal, 13, 3, 2213-2227, 2019.
• Basciftci B., Ahmed S.. Gebraeel N., Data-driven maintenance and operations scheduling in power systems under decision-dependent uncertainty, IISE Transactions, 52, 6, 589-602, 2020.
• Akkermans H. et al., Smart moves for smart maintenance. Findings from a Delphi study on 'Maintenance Innovation Priorities' for the Netherlands, Dutch Institute of World Class Maintenance (DI- WCM), 2016.
• Bokrantz J. et al., Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030, Int. J. Prod. Econ., 191, 154-169, 2017.
• Razmi-Farooji A. et al., Advantages and potential challenges of data management in e-maintenance, Journal of Quality in Maintenance Engineering, 25, 3, 378-396, 2019.
• Runkler T.A., Data analytics: models and algorithms for intelligent data analysis, 2nd ed., Springer Vieweg: Wiesbaden Germany, 2016.
• Baum J. et al., Applications of big data analytics and related technologies in maintenance - literature- based research, Machines, 6, 54, 2018.
• Ansari F., Glawar R., Sihn W., Prescriptive maintenance of CPPS by integrating multimodal data with dynamic Bayesian networks, [in:] Beyerer J., Maier A., Niggemann O. [Eds], Machine Learning for Cyber Physical Systems. Technologien fur die intelli- gente Automation (Technologies for Intelligent Automation), Springer Vieweg, Berlin, Heidelberg, 11, 1-8, 2020.
• Karim R. et al., Maintenance analytics - the new know in maintenance, IFAC-PapersOnLine, 49-28, 214-219, 2016.
• O'Donovan P. et al., An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities, Jour- nal of Big Data, 2, 25, 2015.
• Cortadi A.J. et al., Predictive maintenance on the machining process and machine tool, Applied Sciences, 10, 224, 2020.
• Truong H.L., Integrated analytics for IIoT predictive maintenance using IoT big data cloud systems, 2018 IEEE International Conference on Industrial Internet (ICII), Seattle, WA, 109-118, 2018.
• Bumblauskas D. et al., Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics, Expert Syst. Appl., 90, 303-317, 2017.
• Chandra A.N.R., Jamiy F.E., Reza H., Augmented reality for big data visualization: a review, 2019 International Conference on Computational Science and Computational Intelligence (CSCI) IEEE, 1269-1274, 2019.
• Porcelli I. et al., Technical and organizational issues about the introduction of augmented reality in maintenance and technical assistance services, 11th IFAC Workshop on Intelligent Manufacturing Sys- tems, 257-262, 2013.
• Palmarini R. et al., A systematic review of augmented reality applications in maintenance, Robotics and Computer-Integrated Manufacturing, 49, 215-228, 2018.
• Scurati G.W. et al., Converting maintenance actions into standard symbols for Augmented Reality applications in Industry 4.0, Computers in Industry, 98, 68-79, 2018.
• Ceruti A. et al., Maintenance in aeronautics in an Industry 4.0 context: the role of augmented reality and additive manufacturing, Journal of Computational Design and Engineering, 6, 4, 516-526, 2019.
• Gallala A., Hichri B., Plapper P., Survey: the evolution of the usage of augmented reality in Industry 4.0, IOP Conf. Series: Materials Science and Engineering, 521, 012017, 2019.
• Henderson S., Feiner S., Exploring the benefits of augmented reality documentation for maintenance and repair, IEEE Transactions on Visualization and Computer Graphics, 17, 1355-1368, 2011.
• Nakajima C., Itho N.A., Support system for maintenance training by augmented reality, Proceedings of the 12th International Conference on Image Analysis and Processing Italy 17-19 Sept, 2003, doi: 10.1109/ICIAP.2003.1234043.
• Webel S. et al., An augmented reality training platform for assembly and maintenance skills, Robotics and Autonomous Systems, 61, 4, 398-403, 2013.
• Fiorentino M. et al., Augmented reality on large screen for interactive maintenance instructions, Computers in Industry, 65, 2, 270-278, 2014.
• Masoni R. et al., Supporting remote maintenance in Industry 4.0 through augmented reality, Procedia Manufacturing, 11, 1296-1302, 2017.
• Manuri F., Pizzigalli A., Sanna A.A., State validation system for augmented reality based mainte- nance procedures, Applied Science, 9, 2115, 2019.
• Haritos T., Macchiarella N.D., A mobile application of augmented reality for aerospace maintenance training, 24th Digital Avionics Systems Conference, 30 Oct. - 3 Nov. 2005 Washington, USA, 2005.
• Crescenzio F.D. et al., Augmented reality for aircraft maintenance training and operations support, IEEE Computer Graphics and Applications, 96-101, 2011.
• Yong S.W., Sung A.N., A mobile application of augmented reality for aircraft maintenance of fan cowl door opening, Int. J. Comput. Netw. Inform. Secur., 6, 38-44, 2019.
• Anastassova M., Burkhardt J.M., Automotive technicians' training as a community-of-practice: Implications for the design of an augmented reality teaching aid, Appl. Ergon., 40, 4, 713-721, 2009.
• Quevedo W.X. et al., Virtual reality system for training in automotive mechanics, LNCS, 10324, 185-198, 2017.
• Faieza A.A., Faid A., Lai L.W., Using marker based augmented reality for training in automotive industry, International Journal of Recent Technology and Engineering, 7, 4S2, 118-121, 2018.
• Bottani E., Vignali G., Augmented reality technology in the manufacturing industry: A review of the last decade, IISE Transactions, 51, 3, 284-310, 2019.
• Macchi M. et al., Exploring the role of digital twin for asset lifecycle management, IFAC PapersOn- Line, 51-11, 790-795, 2018.
• Aivaliotis P., Georgoulias K., Chryssolouris G., The use of Digital Twin for predictive maintenance in manufacturing, International Journal of Computer Integrated Manufacturing, 32, 11, 1067-1080, 2019.
• Bevilacqua M. et al., Digital twin reference model development to prevent operators' risk in process plants, Sustainability, 12, 1088, 2020.
• Enders M.R., HoBbach N., Dimensions of digi- tal twin applications - a literature review, 25th Americas Conference on Information Systems, Cancun 2019, https://pdfs.semanticscholar.org/bef1/ dbb393baa428cbf90aaea645a4de6fb2adfe.pdf? _ga= 2.59863273.579588607.1583496369-1381774598.158 3496369.
• Liu Z., Meyendorf N., Mrad N., The role of data fusion in predictive maintenance using digital twin, AIP Conference Proceedings, 1949, 020023, 2018.
• Wang J. et al., Digital twin for rotating machinery fault diagnosis in smart manufacturing, International Journal of Production Research, 57, 12, 39203934, 2019.
• Cattaneo L., Macchi M., A Digital Twin Proof of Concept to Support Machine Prognostics with Low Availability of Run-To-Failure Data, IFAC Paper- sOnLine, 52-10, 37-42, 2019.
• Negri E. et al., A digital twin-based scheduling framework including equipment health index and genetic algorithms, IFAC PapersOnLine, 52-10, 4348, 2019.
• Geissbauer R., Wunderlin J., Lehr J., The future of spare parts is 3D. A look at the challenges and opportunities of 3D printing, PwC, 2017.
• Grubic T., Servitization and remote monitoring technology: A literature review and research agenda, Journal of Manufacturing Technology Management, 1, 100-124, 2014.
• Grubic T. et al., The adoption and use of diagnostic and prognostic technology within UK-based manufacturers, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Man- ufacture, 225, 8, 1457-1470, 2011.
• Senechal O., Performance indicators nomenclatures for decision making in sustainable conditions based maintenance, IFAC PapersOnLine, 51-11, 11371142, 2018.
• Iung B., Levrat E., Advanced maintenance services for promoting sustainability, Procedia CIRP, 22, 1522, 2014.
• Jasiulewicz-Kaczmarek M., Żywica P., The concept of maintenance sustainability performance assessment by integrating balanced scorecard with non-additive fuzzy integral, Eksploatacja i Niezawodność - Maintenance and Reliability, 20, 4, 650-661, 2018.
• Kasava N.K. et al., Sustainable Domain Value Stream Mapping (SdVSM) framework application in aircraft maintenance: a case study, Procedia CIRP, 418-423, 2015.
• Franciosi C. et al., Measuring maintenance impacts on sustainability of manufacturing industries: from a systematic literature review to a framework proposal, J. Clean. Prod., 260, 121065, 2020.
• Hennequin S., Restrepo L.M.R., Fuzzy model of a joint maintenance and production control under sustainability constraint, IFAC-PapersOnLine, 49-12, 1216-1221, 2016.
• Afrinaldi F. et al., Minimizing economic and environmental impacts through an optimal preventive replacement schedule: Model and application, J. Clean. Prod., 143, 882-893, 2017.
• Hoang A., Do P., Iung B., Energy efficiency performance-based prognostics for aided mainte nance decision-making: Application to a manufacturing platform, J. Clean. Prod., 142, 2838-2857, 2017.
• Ighravwe D.E., Oke S.A., Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS, Journal of Manufacturing Technology Management, 28, 7, 961-992, 2017.
• Boral S. et al., A hybrid AI based conceptual decision making model for sustainable maintenance strategy selection, [in:] Chatterjee P. et al., Advanced Multi- Criteria Decision Making for Addressing Complex Sustainability Issues, IGI Global, 63,-90, 2019.
• Singh R.K., Gupta A., Framework for sustainable maintenance system: ISM - fuzzy MICMAC and TOPSIS approach, Annals of Operations Research, 2019.
• Jasiulewicz-Kaczmarek M., Identification of maintenance factors influencing the development of sustainable production processes - a pilot study, IOP Conf. Series: Materials Science and Engineering, 400, no 062014, 2018.
• Fraser K., Hvolby H.H., Tseng T.L., Maintenance management models: a study of the published literature to identify empirical evidence, Int. J. Qual. Reliab. Manag., 32, 6, 635-664, 2015.
• Ajukumar V.N., Gandhi O.P., Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach, J. Clean. Prod., 51, 34-46, 2013.
• Franciosi C., Lambiase A., Miranda S., Sustainable maintenance: a periodic preventive maintenance model with sustainable spare parts management, IFAC PapersOnLine, 50-1, 13692-13697, 2017.
• Takata S. et al., Maintenance: changing role in life cycle management, Annals of the CIRP, 53, 2, 643656, 2004.
• Grabowska M., Takala J., Assessment of quali- ty management system maturity, Lecture Notes in Mechanical Engineering, Advances in Manufacturing, Springer International Publishing, pp. 889-898, 2018.
• Bilge P. et al., Mapping and integrating value creation factors with life-cycle stages for sustainable manufacturing, Procedia CIRP, 61, 28-33, 2017.
• Romero D., Noran O., Towards green sensing virtual enterprises: interconnected sensing enterprises, intelligent assets and smart products in the cyber-physical circular economy, IFAC-PapersOnLine, 50, 1, 11719-11724, 2017.
• Rodseth H., Schjolberg P., Data-driven predictive maintenance for green manufacturing, IWA- MA 2016, Manchester, UK, 36-41, 10-11 November 2016.
• Nielsen I., Dang Q.-V., Nielsen P., Pawlewski P., Scheduling of mobile robots with preemptive tasks, Advances in Intelligent Systems and Computing, 290, 19-27, 2014.
• Thibbotuwawa A., Nielsen P., Banaszak Z., Bocewicz, G., Energy consumption in unmanned aerial vehicles: A review of energy consumption models and their relation to the UAV routing, Advances in Intelligent Systems and Computing, 853, 173-184, 2019.
• Tao F., Zhang M., Nee A.Y.C., Digital twin driven smart manufacturing, Academic Press, 2019.
• Yao X., Sun Z., Li L., Shao H., Joint maintenance and energy management of sustainable manufactucing systems, [in:] ASME 2015 International Manufacturing Science and Engineering Conference, Paper No: MSEC2015-9345, V002T04A008, 2015.
• Gaiardelli P. et al., A classification model for product-service offerings, J. Clean. Prod., 66, 3, 507-519, 2014.
• Zhang Y. et al., A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products, J. Clean. Prod., 142, 2, 626641, 2017.
• Boone C.A., Skipper J.B., Hazen B.T., A framework for investigating the role of big data in service parts management, J. Clean. Prod., 153, 687-691, 2017.
• Bevilacqua M. et al., Big data analytics methodologies applied at energy management in industrial sector: A case study, International Journal of RF Technologies, 8, 3, 105-122, 2017.

Identyfikatory

DOI

0.24425/mper.2020.133730