Investigating Deferment of Maintenance Actions

Abstract When considering a single asset it is plausible to find an optimal solution for its replacement under a wide range of models. Ansell et al (2004) and Ansell and Archibald (2008) considered data driven models which included repair, refurbishment and replacement in the presence of covariates. The covariates may be aspects of the assets, such as age and demand, as well as environmental conditions. Archibald et al (2004) also explored the sensitivity of such solutions to parameter changes in the model. Yet often it is not possible to employ the optimal solutions in a practical context; particularly it may be a requirement to keep assets in operation beyond their repair, refurbishment or even replacement times. Ansell et al (2003) showed that it is possible to assess the impact of maintenance actions (replacement, refurbishment or repair), and by manipulation one can assess the impact of delaying actions. This was based on the assumptions of Cox and Lewis's NHPP model. It is plausible to consider alternative models with more severe degradation models, such as Crow's and others. In many circumstances, though, there is a need to maintain a specific level of availability which adds a constraint. The solution may be a trade-off between availability and timing of maintenance actions. The paper will discuss the modelling issues and through sensitivity analysis and simulation will provide some insights into the ‘best’ solution under differing modelling assumptions.

[1]  Benjamin Gompertz XXI. Supplement to two papers published in the philosophical transactions (1820 and 1825) on the science connected with Human Mortality , 1862, Proceedings of the Royal Society of London.

[2]  Lyn C. Thomas,et al.  The stability of an optimal maintenance strategy for repairable assets , 2004 .

[3]  Jana Vogel Reliability Management Methods And Mathematics , 2016 .

[4]  David R. Cox,et al.  The statistical analysis of series of events , 1966 .

[5]  Larry H. Crow,et al.  Reliability Analysis for Complex, Repairable Systems , 1975 .

[6]  Lyn C. Thomas,et al.  The elixir of life: using a maintenance, repair and replacement model based on virtual and operating age in the water industry , 2004 .

[7]  Peter A. W. Lewis,et al.  Statistical Analysis of Non-Stationary Series of Events in a Data Base System , 1976, IBM J. Res. Dev..

[8]  Simon French,et al.  Statistical Analysis of Reliability Data , 1992 .

[9]  Lyn C. Thomas,et al.  Analysing maintenance data to gain insight into systems performance , 2003, J. Oper. Res. Soc..

[10]  Colin Dennis,et al.  Development and Use of the UK Railway Network’s Safety Risk Model , 2004 .

[11]  John Quigley,et al.  Estimating rate of occurrence of rare events with empirical bayes: A railway application , 2007, Reliab. Eng. Syst. Saf..

[12]  J. T. Duane Learning Curve Approach to Reliability Monitoring , 1964, IEEE Transactions on Aerospace.

[13]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[14]  D. Cox,et al.  The statistical analysis of series of events , 1966 .

[15]  J. Archibald Data Driven Risk Based Management of Assets in the Water Industry , 2008 .

[16]  David Olwell,et al.  Reliability Modeling, Prediction, and Optimization , 2001, Technometrics.

[17]  C. Maclean,et al.  Estimation and testing of an exponential polynomial rate function within the nonstationary Poisson process , 1974 .