This paper proposes a framework based on the Dempster–Shafer Theory of Evidence (DSTE), Possibility Theory (PT) and Fuzzy Random Variables (FRVs) to represent expert knowledge and propagate uncertainty through models. An example of application is given with reference to a check valve of a turbo-pump lubricating system in a Nuclear Power Plant, which is degrading due to mechanical fatigue and undergoes condition-based maintenance interventions. The component degradation-failure model used to evaluate the performance of the maintenance policy contains parameters subject to epistemic uncertainty. vector of the O output variables link between the vector of the input uncertain variables Y _ and that of the output variables Z _ input uncertain variables CDF of Y j Vector of the parameters of the CDF F Y j ( y j ; θ j ) of variable Y j Generic interval provided by the expert for parameter θ Generic confidence level associated to A i i-th interval provided by the expert for the p-th parameter of the j-th input variable Y j Confidence level associated to A i j , p p-th parameter of the j-th random variable Generic uncertain parameter Generic value of θ Necessity measure associated to the set A Possibility measure associated to the set A Possibility function of parameter θ sample a vector for variable Y j made of N T uniform random numbers in [0,1[ Random interval of Y j corresponding to the random number { u j ω } , using the α i -cut [ θ _ j , θ ¯ j ] α i = { [ θ _ j , 1 , θ ¯ j , 1 ] α i , ... , [ θ _ j , M j , θ ¯ j , M j ] α i } α i –cut of θ j Smallest value of the Z 0 component of g, within the intervals [ y _ j ω , y ¯ j ω ] α i , j = 1,2 , ⋯ , k Largest value of the Z 0 component of g, within the intervals [ y _ j ω , y ¯ j ω ] α i , j = 1,2 , ⋯ , k Plausibility measure of set A Belief measure of set A 
Uncertainty treatment in expert information systems for maintenance policy assessment