The purpose of this paper is to discuss operation and maintenance challenges under Arctic conditions and to propose a methodology to assess systems' reliability, maintainability and maintenance costs under the influence of the Arctic operational environment.
A model is suggested for quantifying maintenance costs while taking into account uncertainty due to lack of appropriate data and operational experience using the proportional hazard model and proportional repair model as well as Monte Carlo simulation.
The results show that the operating environment has a considerable influence on the number of failures, the maintenance and repair times and consequently on maintenance cost. Forecasting the maintenance costs based on technical characteristics (e.g. reliability and maintainability) and considering the operational environment, as well as including uncertainty analysis using Monte Carlo simulation, provide more trustworthy information in the decision‐making process.
There are few data and little experience available regarding the operation of offshore oil and gas production systems in the Arctic region. Using the available data collected from similar systems, but in a different operational environment, may result in uncertain or incorrect analysis results. Hence, the method that is used for maintenance cost analysis must be able to quantify the effect of the operating environment on the system reliability and maintainability as well as to quantify the uncertainty.
The paper presents a statistical approach that will be useful in predicting maintenance cost considering the lack of appropriate reliability data from equipment operated in Arctic conditions. The approach presented is valuable for the industrial practitioners in the Arctic region, and may also be adapted to other areas where there is lack of data and operational experience.
Kayrbekova, D., Barabadi, A. and Markeset, T. (2011), "Maintenance cost evaluation of a system to be used in Arctic conditions: a case study", Journal of Quality in Maintenance Engineering, Vol. 17 No. 4, pp. 320-336. https://doi.org/10.1108/13552511111180159Download as .RIS
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