Selecting Suitable Candidates for Predictive Maintenance
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Published
Nov 19, 2020
W. W. Tiddens
A.J.J. Braaksma
T. Tinga
Abstract
Predictive maintenance (PdM) or Prognostics and Health Management (PHM) assists in better predicting the future state of physical assets and making timely and better-informed maintenance decisions. Many companies nowadays desire the implementation of such an advanced maintenance policy. However, the first step in any implementation of PdM is identifying the most suitable candidates (i.e. systems, components). This is to assess where PdM would provide the greatest benefit in performance and costs of downtime. Although multiple selection methods are available, these methods do not always lead to the most suitable candidates for PdM. The main reason is that these methods mainly focus on critical components without considering the clustering of maintenance, and the technical, economic, and organizational feasibility.
This paper proposes a three-stage funnel-based selection method to enhance this process. The first step of the funnel helps to significantly reduce the number of suitable systems or components by a traditional filtering on failure frequency and impact on the firm. In the second and third step, a more in-depth analysis on the remaining candidates is conducted. These steps help to filter potential showstoppers and study the technical and economic feasibility of developing a specific PdM approach for the selected candidates. Finally, the proposed method is successfully demonstrated using two distinct cases: a vessel propulsion system and a canal lock.
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Keywords
Challenges in Prognostics, Component selection, Implementation
References
Alsyouf, I. (2006). Measuring maintenance performance using a balanced scorecard approach. Journal of Quality in Maintenance Engineering, 12(2), 133-149. doi:10.1108/13552510610667165
Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE transactions on dependable and secure computing, 1(1), 11-33.
Banks, J. C., Reichard, K. M., Hines, J. A., & Brought, M. S. (2008). Platform degrader analysis for the design and development of vehicle health management systems. Paper presented at the International Conference on Prognostics and Health Management.
Bengtsson, M. (2008). A Method for Implementing Condition Based Maintenance in Industrial Settings. Paper presented at the 18th international conference on flexible automation and intelligent manufacturing, Skövde.
Bengtsson, M., & Jackson, M. (2004). Important aspects to take into consideration when deciding to implement condition based maintenance. Paper presented at the 17th International Conference on Condition Monitoring and Diagnostic Engineering Management.
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2016). Critical Components Selection for a Prognostics and Health Management System Design: an Application to an Overhead Contact System. Paper presented at the Annual conference of the prognostics and health management society Denver, Colorado.
Dehghanian, P., Fotuhi-Firuzabad, M., Bagheri-Shouraki, S., & Kazemi, A. A. R. (2012). Critical component identification in reliability centered asset management of power distribution systems via fuzzy AHP. IEEE Systems Journal, 6(4), 593-602.
Duplex, P. (2017). Predictive maintenance concepts for maritime applications: PdEng qualifier report. University of Twente. Enschede, Netherlands.
Goossens, A. J., & Basten, R. J. (2015). Exploring maintenance policy selection using the Analytic Hierarchy Process; an application for naval ships. Reliability Engineering & System Safety, 142, 31-41.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics: John Wiley & Sons.
Hashemian, H. M., & Bean, W. C. (2011). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and measurement, 60(10), 3480-3492.
Holmström, J., Ketokivi, M., & Hameri, A. P. (2009). Bridging practice and theory: a design science approach. Decision Sciences, 40(1), 65-87.
Jardine, A. K. S., 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. doi:http://dx.doi.org/10.1016/j.ymssp.2005.09.012
Jonsson, K., & Westergren, U. H. (2004). Developing remote monitoring services: Important points to consider. Paper presented at the 27th Information systems research seminar in scandinavia (Iris 27).
Kar, C., & Mohanty, A. R. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, 20(1), 158-187. doi:https://doi.org/10.1016/j.ymssp.2004.07.006
Labib, A. W. (2004). A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering, 10(3), 191-202.
LaRiviere, J., McAfee, P., Rao, J., Narayanan, V. K., & Sun, W. (2016, 25 may 2016). Where Predictive Analytics Is Having the Biggest Impact. Retrieved from https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact
Lebold, M., Reichard, K., & Boylan, D. (2003, March 8-15, 2003). Utilizing dcom in an open system architecture framework for machinery monitoring and diagnostics. Paper presented at the Aerospace Conference, 2003. Proceedings. 2003 IEEE.
Lee, J., Liao, L., Lapira, E., Ni, J., & Li, L. (2009). Informatics platform for designing and deploying e-manufacturing systems. Collaborative Design and Planning for Digital Manufacturing, 1-35.
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), 314-334.
Lei, X., & Sandborn, P. A. (2016). PHM-based wind turbine maintenance optimization using real options. Int J Progn Health Manag, 7(1), 1-14.
Lewis, R., Dwyer-Joyce, R., Slatter, T., & Brooks, A. (2004). Valve recession: From experiment to predictive model. VDI Berichte(1813), 79-93.
Moubray, J. (1997). RCM II, Reliability-centred maintenance. New York: Industrial Press Inc.
Peeters, J. F. W., Basten, R. J. I., & Tinga, T. (2018). Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliability Engineering & System Safety, 172, 36-44. doi:https://doi.org/10.1016/j.ress.2017.11.024
Pintelon, L., & Van Puyvelde, F. (2006). Maintenance decision making. Leuven, Belgium Acco.
Praveenkumar, T., Saimurugan, M., & Ramachandran, K. I. (2017). Comparison of vibration, sound and motor current signature analysis for detection of gear box faults. International Journal of Prognostics and Health Management, 8(2).
Shafiee, M. (2015). Maintenance strategy selection problem: an MCDM overview. Journal of Quality in Maintenance Engineering, 21(4), 378-402.
Simões, J. M., Gomes, C. F., & Yasin, M. M. (2016). Changing role of maintenance in business organisations: measurement versus strategic orientation. International journal of production research, 54(11), 3329-3346. doi:10.1080/00207543.2015.1106611
Tiddens, W. W., Braaksma, A. J. J., & Tinga, T. (2017). Towards Informed Maintenance Decision Making: Guiding the Application of Advanced Maintenance Analyses. In M. C. Carnero & V. González-Prida (Eds.), Optimum Decision Making in Asset Management (pp. 288-309). Hershey, PA: IGI Global.
Tiddens, W. W., Brouwer, O., Braaksma, A. J. J., & Tinga, T. (2017). The business case for condition-based maintenance: a hybrid (non-) financial approach Paper presented at the Safety and Reliability - Theory and Applications, Portoroz, Slovenia.
Tinga, T., & Loendersloot, R. (2014). Aligning PHM, SHM and CBM by understanding the system failure behaviour. Paper presented at the European Conference of the prognostics and health management society 2014, Nantes.
Tinga, T., Tiddens, W. W., Amoiralis, F., & Politis, M. (2017). Predictive maintenance of maritime systems. Paper presented at the 27th European Safety and Reliability Conference (ESREL 2017).
Tsang, A. H. (1995). Condition-based maintenance: tools and decision making. Journal of Quality in Maintenance Engineering, 1(3), 3-17.
Waeyenbergh, G., & Pintelon, L. (2002). A framework for maintenance concept development. International Journal of Production Economics, 77(3), 299-313.
Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE transactions on dependable and secure computing, 1(1), 11-33.
Banks, J. C., Reichard, K. M., Hines, J. A., & Brought, M. S. (2008). Platform degrader analysis for the design and development of vehicle health management systems. Paper presented at the International Conference on Prognostics and Health Management.
Bengtsson, M. (2008). A Method for Implementing Condition Based Maintenance in Industrial Settings. Paper presented at the 18th international conference on flexible automation and intelligent manufacturing, Skövde.
Bengtsson, M., & Jackson, M. (2004). Important aspects to take into consideration when deciding to implement condition based maintenance. Paper presented at the 17th International Conference on Condition Monitoring and Diagnostic Engineering Management.
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2016). Critical Components Selection for a Prognostics and Health Management System Design: an Application to an Overhead Contact System. Paper presented at the Annual conference of the prognostics and health management society Denver, Colorado.
Dehghanian, P., Fotuhi-Firuzabad, M., Bagheri-Shouraki, S., & Kazemi, A. A. R. (2012). Critical component identification in reliability centered asset management of power distribution systems via fuzzy AHP. IEEE Systems Journal, 6(4), 593-602.
Duplex, P. (2017). Predictive maintenance concepts for maritime applications: PdEng qualifier report. University of Twente. Enschede, Netherlands.
Goossens, A. J., & Basten, R. J. (2015). Exploring maintenance policy selection using the Analytic Hierarchy Process; an application for naval ships. Reliability Engineering & System Safety, 142, 31-41.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics: John Wiley & Sons.
Hashemian, H. M., & Bean, W. C. (2011). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and measurement, 60(10), 3480-3492.
Holmström, J., Ketokivi, M., & Hameri, A. P. (2009). Bridging practice and theory: a design science approach. Decision Sciences, 40(1), 65-87.
Jardine, A. K. S., 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. doi:http://dx.doi.org/10.1016/j.ymssp.2005.09.012
Jonsson, K., & Westergren, U. H. (2004). Developing remote monitoring services: Important points to consider. Paper presented at the 27th Information systems research seminar in scandinavia (Iris 27).
Kar, C., & Mohanty, A. R. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, 20(1), 158-187. doi:https://doi.org/10.1016/j.ymssp.2004.07.006
Labib, A. W. (2004). A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering, 10(3), 191-202.
LaRiviere, J., McAfee, P., Rao, J., Narayanan, V. K., & Sun, W. (2016, 25 may 2016). Where Predictive Analytics Is Having the Biggest Impact. Retrieved from https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact
Lebold, M., Reichard, K., & Boylan, D. (2003, March 8-15, 2003). Utilizing dcom in an open system architecture framework for machinery monitoring and diagnostics. Paper presented at the Aerospace Conference, 2003. Proceedings. 2003 IEEE.
Lee, J., Liao, L., Lapira, E., Ni, J., & Li, L. (2009). Informatics platform for designing and deploying e-manufacturing systems. Collaborative Design and Planning for Digital Manufacturing, 1-35.
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), 314-334.
Lei, X., & Sandborn, P. A. (2016). PHM-based wind turbine maintenance optimization using real options. Int J Progn Health Manag, 7(1), 1-14.
Lewis, R., Dwyer-Joyce, R., Slatter, T., & Brooks, A. (2004). Valve recession: From experiment to predictive model. VDI Berichte(1813), 79-93.
Moubray, J. (1997). RCM II, Reliability-centred maintenance. New York: Industrial Press Inc.
Peeters, J. F. W., Basten, R. J. I., & Tinga, T. (2018). Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliability Engineering & System Safety, 172, 36-44. doi:https://doi.org/10.1016/j.ress.2017.11.024
Pintelon, L., & Van Puyvelde, F. (2006). Maintenance decision making. Leuven, Belgium Acco.
Praveenkumar, T., Saimurugan, M., & Ramachandran, K. I. (2017). Comparison of vibration, sound and motor current signature analysis for detection of gear box faults. International Journal of Prognostics and Health Management, 8(2).
Shafiee, M. (2015). Maintenance strategy selection problem: an MCDM overview. Journal of Quality in Maintenance Engineering, 21(4), 378-402.
Simões, J. M., Gomes, C. F., & Yasin, M. M. (2016). Changing role of maintenance in business organisations: measurement versus strategic orientation. International journal of production research, 54(11), 3329-3346. doi:10.1080/00207543.2015.1106611
Tiddens, W. W., Braaksma, A. J. J., & Tinga, T. (2017). Towards Informed Maintenance Decision Making: Guiding the Application of Advanced Maintenance Analyses. In M. C. Carnero & V. González-Prida (Eds.), Optimum Decision Making in Asset Management (pp. 288-309). Hershey, PA: IGI Global.
Tiddens, W. W., Brouwer, O., Braaksma, A. J. J., & Tinga, T. (2017). The business case for condition-based maintenance: a hybrid (non-) financial approach Paper presented at the Safety and Reliability - Theory and Applications, Portoroz, Slovenia.
Tinga, T., & Loendersloot, R. (2014). Aligning PHM, SHM and CBM by understanding the system failure behaviour. Paper presented at the European Conference of the prognostics and health management society 2014, Nantes.
Tinga, T., Tiddens, W. W., Amoiralis, F., & Politis, M. (2017). Predictive maintenance of maritime systems. Paper presented at the 27th European Safety and Reliability Conference (ESREL 2017).
Tsang, A. H. (1995). Condition-based maintenance: tools and decision making. Journal of Quality in Maintenance Engineering, 1(3), 3-17.
Waeyenbergh, G., & Pintelon, L. (2002). A framework for maintenance concept development. International Journal of Production Economics, 77(3), 299-313.
Section
Technical Papers