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2024 | OriginalPaper | Buchkapitel

A Conceptual Implementation Process for Smart Maintenance Technologies

verfasst von : San Giliyana, Antti Salonen, Marcus Bengtsson

Erschienen in: Advances in Asset Management: Strategies, Technologies, and Industry Applications

Verlag: Springer Nature Switzerland

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Abstract

Industry 4.0 is usually presented as usage of technologies. Some of these play an important role in the development of smart maintenance technologies. However, although the subject of smart maintenance has been discussed for more than 10 years, the manufacturing industry still finds it challenging to implement smart maintenance technologies to add benefits to maintenance organizations in line with company’s goals. This study presents a conceptual process for implementing smart maintenance technologies, challenges and enablers to consider when implementing, and benefits. This article is based on an analysis of empirical findings from seven large manufacturing companies in Sweden, previous maintenance research, and authors’ three previous smart maintenance research articles. In the first article, the authors explored perspectives on smart maintenance technologies from 11 large companies within the manufacturing industry, while in the second one, perspectives on smart maintenance technologies from 15 manufacturing Small and medium-sized enterprises (SMEs) were presented. In the third and final one, the authors developed and presented a testbed for smart maintenance technologies.

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Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive maintenance planning for industry 4.0 using machine learning for sustainable manufacturing. Sustainability, 14(6), 3387. https://doi.org/10.3390/su14063387CrossRef

Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. https://doi.org/10.3390/app12168081CrossRef

Ahmad, R., & Kamaruddin, S. (2012a). An overview of time-based and condition-based maintenance industrial application. Computers & Industrial Engineering, 63, 135–149. https://doi.org/10.1016/j.cie.2012.02.002CrossRef

Ahmad, R., & Kamaruddin, S. (2012b). A review of condition-based maintenance decision-making. European J. Industrial Engineering, 6, 519–541. https://doi.org/10.1504/EJIE.2012.048854CrossRef

Ahmer, M., Marklund, P., Gustafsson, M., & Berglund, K. (2022). An implementation framework for condition-based maintenance in a bearing ring grinder. Procedia CIRP, 107, 746–751. https://doi.org/10.1016/j.procir.2022.05.056CrossRef

Alcácer, V., & Cruz-Machado, V. (2019). Scanning the industry 4.0: A literature review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal, 22(3), 899–891. https://doi.org/10.1016/j.jestch.2019.01.006CrossRef

Al-Najjar, B., Algabroun, H., & Jonsson, M. (2018, September 27–30). Smart maintenance model using cyber physical system. In: International conference on “role of industrial engineering in industry 4.0 paradigm” (ICIEIND), , pp.1–6.

Amruthnath, N., & Gupta, T. (2018, April 26–28). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In: IEEE 5th international conference on industrial engineering and applications (ICIEA), pp. 355–361. https://doi.org/10.13140/RG.2.2.28822.24648

Azevedo, H., Araújo, A., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, 56, 368–379. https://doi.org/10.1016/j.rser.2015.11.032CrossRef

Badri, A., Boudreau-Trudel, B., & Souissi, A. S. (2018). Occupational health and safety in the industry 4.0 era: A cause for major concern? Safety Science, 109, 403–411. https://doi.org/10.1016/j.ssci.2018.06.012CrossRef

Bajic, B., Rikalovic, A., Suzic, N., & Piuri, V. (2021). Industry 4.0 implementation challenges and opportunities: A managerial perspective. IEEE Systems Journal, 15, 546–559. https://doi.org/10.1109/JSYST.2020.3023041CrossRef

Bengtsson, M. (2008, June). A method for implementing condition based maintenance in industrial settings. In 18th international conference on flexible automation and intelligent manufacturing.

Bokrantz, J. (2017). On the transformation of maintenance organisations in digitalised manufacturing. Licentiate thesis, Chalmers University of Technology.

Bokrantz, J., Skoogh, A., Berlin, C., Wuest, T., & Stahre, J. (2020). Smart maintenance: An empirically grounded conceptualization. International Journal of Production Economics, 223, 1–17. https://doi.org/10.1016/j.ijpe.2019.107534CrossRef

Bona, G. D., Cesarotti, V., Arcese, G., & Gallo, T. (2021). Implementation of industry 4.0 technology: New opportunities and challenges for maintenance strategy. Procedia Computer Science, 180, 424–429. https://doi.org/10.1016/j.procs.2021.01.258CrossRef

Cachada, A., Barbosa, J., Leitño, P., Gcraldcs, C. A. S., Deusdado, L., Costa, J., Teixeira, C., Teixeira, J., Moreira, A. H. J., Moreira, P. M., & Romero, L. (2018, September 04–07). Maintenance 4.0: Intelligent and predictive maintenance system architecture. In: IEEE 23rd international conference on emerging technologies and factory automation (ETFA), pp.139-146.

Campos, J., Kans, M., & Salonen, A. (2020). A project management methodology for the digitalisation of the industrial maintenance domain. In Advances in asset management and condition monitoring. https://doi.org/10.1007/978-3-030-57745-2_52CrossRef

Carnero, M. C., & Novés, J. L. (2006). Selection of computerised maintenance management system by means of multicriteria methods. Production Planning & Control, 17, 335–354. https://doi.org/10.1080/09537280600704085CrossRef

Chang, Y. S., Choi, H. C., Sung, S. Y., & Mun, S. J. (2016, July 10–14). A study of cloud based maintenance system architecture for warehouse automation equipment. In: IEEE 5th IIAI international congress on advanced applied informatics (IIAI-AAI), . pp.985-990. https://doi.org/10.1109/IIAI-AAI.2016.190

Chong, S., Pan, G.-T., Chin, J., Show, P. L., Yang, T. C. K., & Huang, C.-M. (2018). Integration of 3D printing and industry 4.0 into engineering teaching. Sustainability, 10(11), 1–13. https://doi.org/10.3390/su10113960CrossRef

Erboz, G. (2017). How to define industry 4.0: Main pillars of industry 4.0. In: Managerial trends in the development of enterprises in globalization era.

Eswaran, M., Gulivindala, A. K., Inkulu, A. K., & Raju Bahubalendruni, M. V. A. (2023). Augmented reality-based guidance in product assembly and maintenance/repair perspective: A state of the art review on challenges and opportunities. Expert Systems with Applications, 213, 118983. https://doi.org/10.1016/j.eswa.2022.118983CrossRef

Figueiredo, M. J. G., Cardoso, P. J. S., Gonçalves, C. D. F., & Rodrigues, J. M. F. (2014, July 16–18). Augmented reality and holograms for the visualization of mechanical engineering parts. In: IEEE 18th international conference on information visualisation, pp. 368-373. https://doi.org/10.1109/IV.2014.17

Flores, M., Maklin, D., Golob, M., Al-Ashaab, A., & Tucci, C. (2018, September 17–19). Awareness towards industry 4.0: Key enablers and applications for internet of things and big data. In: 19th IFIP WG 5.5 working conference on virtual enterprises (PRO-VE). https://doi.org/10.1007/978-3-319-99127-6_32

Fraser, K., Hvolby, H.-H., & Tseng, B. (2015). Maintenance management models: A study of the published literature to identify empirical evidence a greater practical focus is needed. International Journal of Quality & Reliability Management, 32, 635–664.CrossRef

Frost, T., Nöcker, J., Demetz, J., & Schmidt, M. (2019). The evolution of maintenance 4.0 – what should the companies be focusing on now? In: In IncoME-IV.

Giliyana, S., Salonen, A., & Bengtsson, M. (2022, April 26–29). “Perspectives on smart maintenance technologies – A case study in large manufacturing companies.” In: SPS2022, pp.255-266. https://doi.org/10.3233/ATDE220145

Giliyana, S., Bengtsson, M. and Salonen, A. (2023a). Perspectives on smart maintenance technologies – A case study in small and medium-sized enterprises (SMEs) within manufacturing industry. In: 16th WCEAM proceedings, , 5–7 October, 2022, pp.571–581.

Giliyana, S., Karlsson, J., Bengtsson, M., Salonen, A., Adoue, V., & Hedelind, M. (2023b). A Testbed for Smart Maintenance Technologies. In: 7th international congress and workshop on industrial AI and eMaintenance (IAI), 13–15 June 2023.

Goodall, P., Sharpe, R., & West, A. (2019). A data-driven simulation to support remanufacturing operations. Computers in Industry, 105, 48–60. https://doi.org/10.1016/j.compind.2018.11.001CrossRef

James, A., Kumar, G., Khan, A., & Asjad, M. (2022). Maintenance 4.0: Implementation challenges and its analysis. International Journal of Quality & Reliability Management, 40(7), 1706–1728. https://doi.org/10.1108/IJQRM-04-2021-0097CrossRef

Kanawaday, A., & Sane, A. (2017, November 24–26). Machine learning for predictive maintenance of industrial machines using IoT sensor data. In: 8th IEEE international conference on software engineering and service science (ICSESS), , pp. 87-90. https://doi.org/10.1109/ICSESS.2017.8342870

Kans, M., & Galar, D. (2017, November 28). The impact of maintenance 4.0 and big data analytics within strategic asset management. In Maintenance, performance, measurement & management (pp. 96–104).

Kour, H., & Gondhi, N. K. (2020). Machine learning techniques: A survey. In: ICIDCA2019.

Labib, A. (2004). A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering, 10, 191–202. https://doi.org/10.1108/13552510410553244CrossRef

Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M., & Sutheralnd, J. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, 80, 506–511. https://doi.org/10.1016/j.procir.2018.12.019CrossRef

Lewis, S. A., & Edwards, T. G. (1997, October 30). Smart sensors and system health management tools for avionics and mechanical systems. In 16^th IEEE digital avionics systems conference (DASC) (pp. 8.5–8.1). https://doi.org/10.1109/DASC.1997.637283CrossRef

Liu, Y., & Xu, X. (2017). Industry 4.0 and cloud manufacturing: A comparative analysis. Journal of Manufacturing Science and Engineering, 139, 1–8. https://doi.org/10.1115/MSEC2016-8726CrossRef

Lundgren, C., Bokrantz, J., & Skoogh, A. (2021). A strategy development process for smart maintenance implementation. Journal of Manufacturing Technology Management, 32(9), 142–166. https://doi.org/10.1108/JMTM-06-2020-0222CrossRef

Lundgren, C., Bokrantz, J., & Skoogh, A. (2022, April 26–29). Hindering factors in smart maintenance implementation. In: SPS2022, pp.629-637. https://doi.org/10.3233/ATDE220181

Masoni, R., Ferrise, F., Bordegoni, M., Gattullo, M., Uva, A. E., Fiorentino, M., Carrabba, E., & Di Donato, M. (2017). Supporting remote maintenance in industry 4.0 through augmented reality. Procedia Manufacturing, 11, 1296–1302. https://doi.org/10.1016/j.promfg.2017.07.257CrossRef

Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261CrossRef

Matt, D., Modrák, V., & Zsifkovits, H. (2020). Industry 4.0 for SMEs challenges, opportunities and requirements. Palgrave Macmillan Cham.CrossRef

Maxwell, J. (1996). Qualitative research design: An interactive approach. Sage Publications, Inc.

Moeuf, A., Lamouri, S., Pellerin, R., Tamayo-Giraldo, S., Tobon-Valencia, E., & Eburdy, R. (2020). Identification of critical success factors, risks and opportunities of industry 4.0 in SMEs. International Journal of Production Research, 58, 1384–1400. https://doi.org/10.1080/00207543.2019.1636323CrossRef

Moubray, J. (1997). Reliability-centred maintenance: [RCM II]. Butterworth Heinemann.

Patton, M. (1990). Qualitative evaluation and research methods. Sage Publications, Inc.

Prajapati, A., & Ganesan, S. (2013). Application of statistical techniques and neural networks in condition-based maintenance. Quality and Reliability Engineering International, 29. https://doi.org/10.1002/qre.1392

Rastegari, A. (2017). Condition based maintenance in the manufacturing industry: From strategy to Implementation. Doct. thesis,. Mälardalen University.

Rikalovic, A., Suzic, N., Bajic, B., & Piuri, V. (2021). Industry 4.0 implementation challenges and opportunities: A technological perspective. IEEE Systems Journal, 1–14. https://doi.org/10.1109/JSYST.2021.3101673

Roy, R., Stark, R., Tracht, K., Takata, S., & Mori, M. (2016). Continuous maintenance and the future – Foundations and technological challenges. CIRP Annals, 65(2), 667–688. https://doi.org/10.1016/j.cirp.2016.06.006CrossRef

Säfsten, K., & Gustavsson, M. (2020). Research methodology – for engineers and other problem-solvers. Studentlitteratur AB.

Silva, D., Carvalho, L. I., Soares, J., & Sofia, R. C. (2021). A performance analysis of internet of things networking protocols: Evaluating MQTT, CoAP, OPC UA. Applied Sciences, 11(11), 4879.CrossRef

Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, 1–16. https://doi.org/10.1016/j.compind.2020.103335CrossRef

Singh, S., Galar, D., Baglee, D., & Björling, S.-E. (2013). Self-maintenance techniques: A smart approach towards self-maintenance system. International Journal of System Assurance Engineering and Management, 5(1), 75–83. https://doi.org/10.1007/s13198-013-0200-7CrossRef

Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192–204. https://doi.org/10.1016/j.iotcps.2023.04.006CrossRef

Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., & Bokrantz, J. (2018). A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Computers & Industrial Engineering, 125, 533–544. https://doi.org/10.1016/j.cie.2018.04.024CrossRef

Tao, F., Qi, Q., Wang, L., & Nee, A. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014CrossRef

Witkowski, K. (2017). Internet of things, big data, industry 4.0 – Innovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763–769. https://doi.org/10.1016/j.proeng.2017.03.197CrossRef

Yin, S., & Kaynak, O. (2015). Big data for modern industry: Challenges and trends [point of view]. Proceedings of the IEEE, 103(2), 143–146. https://doi.org/10.1109/JPROC.2015.2388958CrossRef

Yuan, J., & Liu, X. (2013). Semi-supervised learning and condition fusion for fault diagnosis. Mechanical Systems and Signal Processing, 38, 615–627. https://doi.org/10.1016/j.ymssp.2013.03.008CrossRef

Titel: A Conceptual Implementation Process for Smart Maintenance Technologies
verfasst von: San Giliyana
Antti Salonen
Marcus Bengtsson
Verlag: Springer Nature Switzerland
Buch: Advances in Asset Management: Strategies, Technologies, and Industry Applications
Print ISBN: 978-3-031-52390-8

Electronic ISBN: 978-3-031-52391-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-52391-5_3

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