Due to uncertainties in the deterioration process of long service-life assets, assessing reliability and planning maintenance and inspection activities are often difficult tasks. Most of the methods developed use operational or historical data from the manufacturer to predict the deterioration of the asset and estimate its reliability. However, in practice, such failure data is often scarce (e.g. very rare events) and the reliability prediction models might not be adapted to the operation of the user. In addition, available data gathered from maintenance or inspection tasks might only inform about a short period of time, and it might be difficult to obtain the corresponding reliability predictions as the associated uncertainty is still significant. Hence, this paper introduces a reliability assessment method that quantifies uncertainties regarding the failure of long service-life components. It combines Cooke’s classical method (also known as Structured Expert Judgement) with a histogram technique, creating a performance-based method to assess uncertainty in components lifetime distributions. A case study on railway wheelsets is analysed to illustrate the effectiveness of such approach. The results show that the presented method is able to assess failure rates, which can support decisions in reliability-based maintenance of engineering systems.
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