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Abstract—Asset failures, that needs to be managed, has an uncertain characteristic and analysis of uncertainty is essential to Asset Management (AM). Forecasting the technical performance of assets forms an integral part of strategic and operational activities within AM. To establish the failure behaviour of assets requires a significant degree of reliable asset information, which, in many practical cases, is not sufficiently rich or available to provide a basis for straightforward decision-making. In this paper a practical and systematic statistical methodology is used for dealing with incomplete asset lifetime data. The method described in this paper is based on a statistical parametric method and is applied with the aim of obtaining an indicator of the future failure expectancy with a certain confidence interval. On the whole, the paper concludes that, even though input data was either missing or incomplete, it is in certain cases possible to develop sensible probability models. These models take into account uncertainty and ultimately can be applied to facilitate the asset manager in AM decision-making. In addition to applying statistical methods, this contribution highlights the vital role of engineering and expert knowledge in interpreting the statistical results.