Impact of uncertainty for prognostics of complex systems

This paper shed lights on an approach for computational framework for uncertainty impact, on one hand, and quantification in prognostics in terms of condition-based monitoring of complex systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the first-order reliability methods (FORM) to quantify the uncertainty in the remaining useful life (RUL) algorithms is a major factor of prognosis frameworks failure because it pushes mathematical foundation to result uncertainty in long term predictions if it is combined with outer feedback correction loops for interpreting uncertainty in online monitoring. Prognostic consists of two important steps. To begin the condition on the sate systems are calculated approximatively by the probability distribution of the damage parameters, then, the rest of the state's systems are presaged until failure, that is why the remaining useful life of the system is the main purpose of extending the remaining useful life of a faulty system.