Supporting the Implementation of Predictive Maintenance a Process Reference Model

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Published Mar 24, 2021
Carolin Wagner Bernd Hellingrath

Abstract

The perception of predictive maintenance as a proactive maintenance strategy to anticipate and reduce severe and costly failures and by thus increasing asset reliability has grown significantly in recent years. Due to the availability of machine sensor data and the intention to use these data in a purposeful way, the introduction of predictive maintenance provides a logical step towards maintenance optimization in industry. Several German industrial surveys highlight the growing interest in the topic by the majority of the addressed companies. Nevertheless, most of these companies are considering predictive maintenance on their future agenda and are currently only at the beginning of its implementation. This is, in many cases, due to missing internal knowledge and systematic guidance for maintenance practitioners. Existing process models and supportive guidance build on theoretical knowledge from experts; however, they often lack the complexity and challenges of industrial applications. In addition, most theoretical models focus on specific aspects of the entire process, target particular application areas, or present a few high-level steps. This paper, therefore, introduces the Process Reference Model for Predictive Maintenance (PReMMa), a comprehensive three-stage hierarchical process reference model for the implementation of predictive maintenance for industrial applications. The process reference model synthesizes existing process models as well as results from interviews with eleven practitioners from both management consultancies and experts from several industrial fields. With regard to four main phases and the predictive maintenance application, results are presented with consideration of the essential steps, their deliverables as well as the involved persons.

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Keywords

predictive maintenance, prognostics and health management, Process Reference Model, Industry Insights

References
Adesola, S., & Baines, T. (2005). Developing and evaluating a methodology for business process improvement. Business Process Management Journal, 11(1), 37–46. https://doi.org/10.1108/14637150510578719
Aeronautical Design Standard (2013). Aeronautical Design Standard Handbook. (ADS, 79D-HDBK).
Bird, J., Madge, N., & Reichard, K. (2014). Towards a capabilities taxonomy for prognostics and health management. International Journal of Prognostics and Health Management, 5(2).
Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A proactive decision making framework for condition-based maintenance. Industrial Management & Data Systems, 115(7), 1225–1250. https://doi.org/10.1108/IMDS-03-2015-0071
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2016). Development of a prognostics and health management system for the railway infrastructure — Review and methodology. In M. J. Zuo (Ed.), Proceedings of 2016 Prognostics and System Health Management Conference (PHM-Chengdu): October 19-21, 2016, Chengdu, Sichuan, China (pp. 1–8). Piscataway, NJ: IEEE. https://doi.org/10.1109/PHM.2016.7819783
Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic Analysis. In P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (Vol. 3, pp. 843–860). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-5251-4_103
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide.
Coble, J. B. (2010). Merging Data Sources to Predict Remaining Useful Life - An Automated Method to Identify Prognostic Parameters.
Cocconcelli, M., Capelli, L., Cavalaglio Camargo Molano, J., & Borghi, D. (2018). Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field. Machines, 6(2), 17. https://doi.org/10.3390/machines6020017
Das, S. (2015). An efficient way to enable prognostics in an onboard system. In IEEE Aerospace Conference, 2015: 7 - 14 March 2015, Big Sky, MT (pp. 1–7). Piscataway, NJ, Piscataway, NJ: IEEE. https://doi.org/10.1109/AERO.2015.7118976
Elattar, H. M., Elminir, H. K., & Riad, A. M. (2016). Prognostics: a literature review. Complex and Intelligent Systems. (2), 125–154.
Feldmann, S., Herweg, O., Rauen, H., & Synek, P.‑M. (2017). Predictive Maintenance: Service der Zukunft - und wo er wirklich steht.
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I., & Celaya, J. R. (2017). Prognostics: The Science of Prediction: CreateSpace Independent Publishing.
Guillén, A. J., Crespo, A., Macchi, M., & Gómez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning & Control, 27(12), 991–1004.
Haddad, G., Sandborn, P. A., & Pecht, M. G. (2012). An Options Approach for Decision Support of Systems With Prognostic Capabilities. IEEE Transactions on Reliability, 61(4), 872–883. https://doi.org/10.1109/TR.2012.2220699
Hahn, A. (2016). Operational Technology and Information Technology in Industrial Control Systems. In E. J. M. Colbert & A. Kott (Eds.), Advances in Information Security: Vol. 66. Cyber-security of SCADA and Other Industrial Control Systems (pp. 51–68). Cham, s.l.: Springer International Publishing. https://doi.org/10.1007/978-3-319-32125-7_4
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. https://doi.org/10.1016/j.ymssp.2008.06.009
Hu, C., Youn, B. D., Wang, P., & Taek Yoon, J. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120–135. https://doi.org/10.1016/j.ress.2012.03.008
IEEE Reliability Society. IEEE Standard Framework for Prognostics and Health Management of Electronic Systems. (IEEE, Std 1856-2017). Piscataway, NJ, USA: IEEE.
International Organization for Standardization. Condition monitoring and diagnostics of machines — Prognostics — Part 1: General guidelines. (ISO , 13381-1:2015). International Organization for Standardization (2003).
Condition monitoring and diagnostics of machines - Data processing, communication and presentation - Part 1: General guidelines. (ISO, 13374-1:2003).
International Organization for Standardization (2012). Condition monitoring and diagnostics of machines. (ISO, 13379-1:2012).
International Organization for Standardization (2018). Condition monitoring and diagnostics of machines - General guidelines. (ISO, 17359-1:2018).
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012
KDnuggets (2014). What main methodology are you using for your analytics, data mining, or data science projects? Poll. Retrieved from https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-methodology.html
Kvale, S., & Brinkmann, S. (2009). Interviews: Learning the craft of qualitative research interviewing (2. ed.). Los Angeles: SAGE.
Lee, J., Jin, C., Liu, Z., & Davari Ardakani, H. (2017). Introduction to Data-Driven Methodologies for Prognostics and Health Management. In S. Ekwaro-Osire, A. C. Gonçalves, & F. M. Alemayehu (Eds.), SpringerLink : Bücher. Probabilistic Prognostics and Health Management of Energy Systems (pp. 9–32). Cham: Springer. https://doi.org/10.1007/978-3-319-55852-3_2
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). Intelligent prognostics tools and e-maintenance. Computers in Industry, 57(6), 476–489. https://doi.org/10.1016/j.compind.2006.02.014
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1-2), 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
Lei, Y., Guo, L., Li, N., & Yan, T. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
Li, R., Verhagen, W. J., & Curran, R. (2018). A Functional Architecture of Prognostics and Health Management using a Systems Engineering Approach. European Conference of the Prognostics and Health Management Society 2018.
Matook, S., & Indulska, M. (2009). Improving the quality of process reference models: A quality function deployment-based approach. Decision Support Systems, 47(1), 60–71. https://doi.org/10.1016/j.dss.2008.12.006
MIMOSA (1998-2019). Open Standards for Physical Assets. Retrieved from http://www.mimosa.org/
Misoch, S. (2019). Qualitative Interviews (2., erweiterte Auflage). De Gruyter Studium. Berlin: De Gruyter Oldenbourg.
Project Management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide)—Sixth Edition (ENGLISH) (6th ed.). Newtown Square, PA: Project Management Institute.
Saxena, A., Roychoudhury, I., Celaya, J. R., Saha, S., Saha, B., & Goebel, K. (2010). Requirements specifications for prognostics: An overview. American Institute of Aeronautics and Astronautics.
Shi, W., & Dustdar, S. (2016). The Promise of Edge Computing. Computer, 49(5), 78–81. https://doi.org/10.1109/MC.2016.145
Shi, Z., Lee, J., & Cui, P. (2016). Prognostics and health management solution development in LabVIEW: Watchdog agent® toolkit and case study. In M. J. Zuo (Ed.), Proceedings of 2016 Prognostics and System Health Management Conference (PHM-Chengdu): October 19-21, 2016, Chengdu, Sichuan, China (pp. 1– 6). Piscataway, NJ: IEEE. https://doi.org/10.1109/PHM.2016.7819780
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). NISTIR 8012: Standards related to prognostics and health management (PHM) for manufacturing.
Voisin, A., Levrat, E., Cocheteux, P., & Iung, B. (2010). Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed. Journal of Intelligent Manufacturing, 21(2), 177–193. https://doi.org/10.1007/s10845-008-0196-z
Wagner, C., & Hellingrath, B. (2019). Implementing Predictive Maintenance in a Company: Industry Insights with Expert Interviews. 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).
Walden, D. D., Roedler, G. J., Forsberg, K., Hamelin, R. D., & Shortell, T. M. (Eds.). (2015). Systems engineering handbook: A guide for system life cycle processes and activities ; INCOSE-TP-2003-002-04, 2015 (4. edition). Hoboken, NJ: Wiley.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii–xxiii.
Widera, A., & Hellingrath, B. (2011). Improving Humanitarian Logistics - Towards a Tool-based Process Modeling Approach, 273–295.
Xue, F., Bonissone, P., Varma, A., Yan, W., Eklund, N., & Goebel, K. (2008). An Instance-Based Method for Remaining Useful Life Estimation for Aircraft Engines. Journal of Failure Analysis and Prevention, 8(2), 199– 206. https://doi.org/10.1007/s11668-008-9118-9
Zaidan, M. A. (2014). Bayesian Approaches for Complex System Prognostics: University of Sheffield.
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