Advertisement

Risk- and Condition-Based Maintenance

  • Christer StenströmEmail author
  • Sarbjeet Singh
Chapter
  • 336 Downloads
Part of the Asset Analytics book series (ASAN)

Abstract

Condition-based maintenance (CBM) strategies have increased in recognition over the last decades, and continues to do so with an internationalized market and cheaper sensor technology. CBM is in many cases the most effective approach to maintenance, considering risk, resource use, sustainability, safety and cost. Thus, CBM is often feasible both from a life-cycle cost (LCC) perspective and a life cycle analysis (LCA) perspective. In this chapter, we will study risk-based and condition-based maintenance from a maintenance and reliability perspective. After a brief background, we will discuss the necessary conditions for CBM to be a feasible strategy for optimized usage of equipment. On the operational level, CBM can be on schedule, on request or on a continuous monitoring basis. Thus, the technologies used for CBM can broadly be divided into continuous monitoring, which often is simply called condition monitoring, and into non-destructive testing (NDT), for periodic inspections. Therefore, two sections are dedicated to condition monitoring and NDT. Additional techniques for CBM and risk assessment will be discussed in the section thereafter. Lastly, we will look briefly into the continuously growing topic of prognostics.

Keywords

Condition-based maintenance Non-destructive testing Failure mechanisms Risk assessment Reliability modelling Prognostics 

Notes

Acknowledgements

The authors would like to thank Dr. Madhav Mishra, Luleå University of Technology, for his valuable comments that improved this chapter.

References

  1. AAIC. (1987). Aircraft accident investigation report.Google Scholar
  2. Al-Chalabi, H., Lundberg, J., Ahmadi, A., et al. (2015). Case study: Model for economic lifetime of drilling machines in the Swedish mining industry. The Engineering Economist, 60(2), 138–154.  https://doi.org/10.1080/0013791X.2014.952466.CrossRefGoogle Scholar
  3. ASCE. (2011). Failure to act: The economic impact of current investment trends in surface transportation infrastructure.Google Scholar
  4. Ben-Daya, M., Kumar, U., & Murthy, D. N. P. (2016). Introduction to maintenance engineering: Modelling, optimization and management.Google Scholar
  5. CEN. (2010). EN 13306: Maintenance terminology.Google Scholar
  6. Drenick, R. F. (1960). The failure law of complex equipment. Journal of the Society for Industrial and Applied Mathematics, 8(4), 680–690.CrossRefGoogle Scholar
  7. Frangopol, D. M. (2011). Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges. Structure and Infrastructure Engineering, 7(6), 389–413.  https://doi.org/10.1080/15732471003594427.CrossRefGoogle Scholar
  8. Goebel, K., Saha, B., Saxena, A., et al. (2008). Prognostics in battery health management. IEEE Instrumentation and Measurement Magazine, 11(4), 33–40.  https://doi.org/10.1109/MIM.2008.4579269.CrossRefGoogle Scholar
  9. IEC. (2015). IEC 60050-192:2015: International electrotechnical vocabulary—Part 192: Dependability.Google Scholar
  10. ISO. (2009). ISO 31000:2009: Risk management—Principles and guidelines.Google Scholar
  11. ISO/IEC. (2009). ISO/IEC 31010: Risk management: Risk assessment techniques.Google Scholar
  12. Kececioglu, D. (1991). Reliability engineering handbook (Vol. 2).Google Scholar
  13. Lei, Y., Li, N., Guo, L., et al. (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.CrossRefGoogle Scholar
  14. McInerney, P. (2005). Special commission of inquiry into the waterfall rail accident (Final Report).Google Scholar
  15. Mishra, M., Odelius, J., Thaduri, A., et al. (2017). Particle filter-based prognostic approach for railway track geometry. Mechanical Systems and Signal Processing, 96, 226–238.  https://doi.org/10.1016/j.ymssp.2017.04.010.CrossRefGoogle Scholar
  16. Murphy, K. E., Carter, C. M., & Brown, S. O. (2002). The exponential distribution: The good, the bad and the ugly. A practical guide to its implementation. In Proceedings of Annual Reliability and Maintainability Symposium (pp. 550–555).  https://doi.org/10.1109/rams.2002.981701.
  17. NASA. (2007). Systems engineering handbook.Google Scholar
  18. Nectoux, P., Gouriveau, R., Medjaher, K., et al. (2012) PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management (pp. 1–8).Google Scholar
  19. Nowlan, F. S., & Heap, H. F. (1978). Reliability-centered maintenance.Google Scholar
  20. OECD. (2006). Infrastructure to 2030: Telecom, land transport, water and electricity.  https://doi.org/10.1787/9789264043466-en.
  21. ORR. (2006). Train derailment at Hatfield: A final report by the independent investigation board.Google Scholar
  22. Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.  https://doi.org/10.1016/j.ymssp.2010.07.017.CrossRefGoogle Scholar
  23. Rausand, M., & Høyland, A. (2004). System reliability theory: Models, statistical methods, and applications.Google Scholar
  24. Roe, G. J., & Bramfitt, B. L. (1990). Notch toughness of steels (pp. 737–754).Google Scholar
  25. Stenström, C., Carlson, J. E., & Lundberg, J. (2015). Condition monitoring of cracks and wear in mining mills using water squirter ultrasonics. International Journal of Condition Monitoring, 5(1), 2–8.CrossRefGoogle Scholar
  26. Uckun, S., Goebel, K., & Lucas, P. J. F. (2008). Standardizing research methods for prognostics. In International Conference on Prognostics and Health Management, PHM.  https://doi.org/10.1109/phm.2008.4711437.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Division of Operation and MaintenanceLuleå University of TechnologyLuleåSweden
  2. 2.Mechanical Engineering DepartmentGovernment College of Engineering and Technology, JammuJammuIndia

Personalised recommendations