Optimum maintenance strategy under uncertainty in the lifetime distribution

• Published in: Reliability engineering & system safety Vol. 133; pp. 59 - 67
• Main Authors: de Jonge, Bram, Klingenberg, Warse, Teunter, Ruud, Tinga, Tiedo
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.2015; Elsevier
• Subjects: Age-based preventive maintenance; Applied sciences; Computer programs; Decision theory. Utility theory; Exact sciences and technology; Handbooks; Industrial metrology. Testing; Maintenance; Maintenance planning; Mechanical engineering. Machine design; Operational research and scientific management; Operational research. Management science; Optimization; Parameter uncertainty; Reliability engineering; Reliability theory. Replacement problems; Software reliability; Strategy; Uncertainty; Parameter uncertainty; Age-based preventive maintenance; Maintenance planning; Uncertainty; Decision making; Rupture; Durability; Software reliability; Maintenance; Cost benefit analysis; System reliability; Modeling; Preventive maintenance; Optimization; Lifetime; Uniform distribution; Optimal strategy; Uncertain system; Systems engineering; Planning; Weibull distribution; Age; Software engineering
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2014.09.013

The problem of determining the optimal maintenance strategy for a machine given its lifetime distribution has been studied extensively. Solutions to this problem are outlined in the academic literature, prescribed in professional handbooks, implemented in reliability engineering software systems and widely used in practice. These solutions typically assume that the lifetime distribution and its parameter values are known with certainty, although this is usually not the case in practice. In this paper we study the effect of parameter uncertainty on the optimum age-based maintenance strategy. The effect of uncertainty is evaluated by considering both a theoretical uniform lifetime distribution and a more realistic Weibull lifetime distribution. The results show that admitting to the uncertainty does influence the optimal maintenance age and also provides a quantifiable cost benefit. The results can help maintenance managers in making maintenance decisions under uncertainty, and also in deciding when it is worthwhile to invest in advanced data improvement procedures. •The effect of parameter uncertainty on the optimum age-based maintenance strategy is studied.•A theoretical uniform and a realistic Weibull distribution are considered.•Admitting to the uncertainty provides a quantifiable cost benefit.•The maintenance age first decreases and then increases as the uncertainty increases.