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STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

Published online by Cambridge University Press:  27 July 2021

Julie Krogh Agergaard ,
Kristoffer Vandrup Sigsgaard ,
Niels Henrik Mortensen ,
Jingrui Ge ,
Kasper Barslund Hansen  and
Waqas Khalid
Julie Krogh Agergaard*
Affiliation:
Technical University of Denmark
Kristoffer Vandrup Sigsgaard
Affiliation:
Technical University of Denmark
Niels Henrik Mortensen
Affiliation:
Technical University of Denmark
Jingrui Ge
Affiliation:
Technical University of Denmark
Kasper Barslund Hansen
Affiliation:
Technical University of Denmark
Waqas Khalid
Affiliation:
Technical University of Denmark
*
Agergaard, Julie Krogh, Technical University of Denmark, Mechanical Engineering, Denmark, [email protected]

Abstract

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Maintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.

This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.

Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Keywords

ModularizationMaintenanceKnowledge managementLarge-scale engineering systemsBig data
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2701 - 2710
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Assaf, D. and Shanthikumar, G.J. (1987), “Optimal group maintenance policies with continuous and periodic inspections”, Management Science, Vol. 33 No. 1, pp. 1440–1452.CrossRefGoogle Scholar
Standards, BSI: British. (2016), “ISO 14224:2016”.Google Scholar
Cui, L. and Li, H. (2006), “Opportunistic maintenance for multi-component shock models”, Mathematical Methods of Operations Research, Vol. 63 No. 3, pp. 493–511.CrossRefGoogle Scholar
Standard, Dansk. (2017), DS/EN 13306:2017 Maintenance - Maintenance Terminology.Google Scholar
Dekker, R. and Smeitink, E. (1991), “Opportunity-based block replacement”, European Journal of Operational Research, Vol. 53 No. 1, pp. 46–63.CrossRefGoogle Scholar
Dekker, R., Wildeman, R.E. and Van Der Duyn Schouten, F.A. (1997), “A review of multi-component maintenance models with economic dependence”, Mathematical Methods of Operations Research, Physica-Verlag, Vol. 45 No. 3, pp. 411–435.CrossRefGoogle Scholar
Guo, C., Lyu, C., Chen, J. and Zhou, D. (2018), “A multi-event combination maintenance model based on event correlation”, PLoS ONE, Public Library of Science, Vol. 13 No. 11, available at:https://doi.org/10.1371/journal.pone.0207390.CrossRefGoogle ScholarPubMed
Harlou, U. (2006), Developing Product Families Based on Architectures, Contribution to a Theory of Product Families.Google Scholar
Hodkiewicz, M. and Ho, M.T.W. (2016), “Cleaning historical maintenance work order data for reliability analysis”, Journal of Quality in Maintenance Engineering, Emerald Group Publishing Ltd., Vol. 22 No. 2, pp. 146–163.Google Scholar
Hu, J. and Zhang, L. (2014), “Risk based opportunistic maintenance model for complex mechanical systems”, Expert Systems with Applications, Vol. 41 No. 6, pp. 3105–3115.CrossRefGoogle Scholar
Li, G., Li, Y., Zhang, X., Hou, C., He, J., Xu, B. and Chen, J. (2018), “Development of a preventive maintenance strategy for an automatic production line based on group maintenance method”, Applied Sciences (Switzerland), MDPI AG, Vol. 8 No. 10, available at:https://doi.org/10.3390/app8101781.Google Scholar
Løkkegaard, M., Mortensen, N.H. and McAloone, T.C. (2016), “Towards a framework for modular service design synthesis”, Research in Engineering Design, Springer London, Vol. 27 No. 3, pp. 1–13.CrossRefGoogle Scholar
Martínez, L.B., Márquez, A.C., Gunckel, P.V. and Andreani, A.A. (2013), “The graphical analysis for maintenance management method: A quantitative graphical analysis to support maintenance management decision making”, Quality and Reliability Engineering International, Vol. 29 No. 1, pp. 77–87.CrossRefGoogle Scholar
Meyer, M.H. and Lehnerd, A.P. (1997), The Power of Product Platforms, The Free Press.Google Scholar
Navaei, J. and ElMaraghy, H. (2014), “Grouping product variants based on alternate machines for each operation”, Procedia CIRP, Vol. 17, Elsevier B.V., pp. 61–66.Google Scholar
Nzukam, C., Voisin, A., Levrat, E., Sauter, D. and Iung, B. (2017), “A dynamic maintenance decision approach based on maintenance action grouping for HVAC maintenance costs savings in Non-residential buildings”, IFAC-PapersOnLine, Elsevier B.V., Vol. 50 No. 1, pp. 13722–13727.CrossRefGoogle Scholar
Poppe, J., Boute, R.N. and Lambrecht, M.R. (2018), “A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds”, European Journal of Operational Research, Elsevier B.V., Vol. 268 No. 2, pp. 515–532.CrossRefGoogle Scholar
Ruschel, E., Santos, E.A.P. and Loures, E. de F.R. (2017), “Industrial maintenance decision-making: A systematic literature review”, Journal of Manufacturing Systems, Elsevier B.V., Vol. 45, pp. 180–194.CrossRefGoogle Scholar
Van Do, P., Barros, A., Bérenguer, C., Bouvard, K. and Brissaud, F. (2013), “Dynamic grouping maintenance with time limited opportunities”, Reliability Engineering and System Safety, Elsevier Ltd, Vol. 120, pp. 51–59.CrossRefGoogle Scholar
Voss, C.A. and Hsuan, J. (2009), “Service Architecture and Modularity”, Decision Sciences, Vol. 40 No. 3, pp. 541–569.CrossRefGoogle Scholar
Vu, H.C., Do, P. and Barros, A. (2018), “A Study on the Impacts of Maintenance Duration on Dynamic Grouping Modeling and Optimization of Multicomponent Systems”, IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers Inc., Vol. 67 No. 3, pp. 1377–1392.CrossRefGoogle Scholar
Vu, H.C., Do, P., Barros, A. and Bérenguer, C. (2014), “Maintenance grouping strategy for multi-component systems with dynamic contexts”, Reliability Engineering and System Safety, Vol. 132, pp. 233–249.CrossRefGoogle Scholar
Wu, T., Yang, L., Ma, X., Zhang, Z. and Zhao, Y. (2020), “Dynamic maintenance strategy with iteratively updated group information”, Reliability Engineering and System Safety, Elsevier Ltd, Vol. 197, available at:https://doi.org/10.1016/j.ress.2020.106820.CrossRefGoogle Scholar
Zhang, C., Gao, W., Guo, S., Li, Y. and Yang, T. (2017), “Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance”, Renewable Energy, Elsevier Ltd, Vol. 103, pp. 606–612.CrossRefGoogle Scholar