In most industrial fields, the optimization of maintenance policies has become a key issue. To address this problem, predictive maintenance seems to be one of the most effective known approaches. It consists of driving the maintenance process anticipating the evolution of the system state. A prediction process, called ”prognostic” could also be introduced and it lays on the online estimation of the remaining useful life (RUL). Dynamic bayesian networks (DBN) have been proved as relevant to perform reliability analysis as they can easily represent complex systems behaviors. Based on this formalism, graphical duration models (GDM) were developed by R.Donat (2009) to set all kind of sojourn time distributions for each state of the system. Unlike to some Markovian modelling that imposes exponential behavior, this approach could better model the exact degradation dynamic of real industrial systems. (F.V.Jensen 2007) (J.Pearl 1985) In our study, where time and states spaces are discretized, a single component degradation whom process is assumed to be monotone increasing, is considered. The real state of the system is unknown but periodically observed by monitoring devices, characterized by their good detections rates, false alarms rates and non detection rates (summarized in a confusion matrix as a conditional probability table of the considered DBN). This paper introduces an online RUL estimation algorithm based on the use of graphical duration models. The objective is both to evaluate the RUL and to adjust it, in real time, when a new observation is available. Then, the algorithm is applied to a test case to evaluate the quality of the RUL estimations. This paper will also provide comparative results in respect of the considered degradation model (Markovian approach vs GDM). Finally, these RUL computations, and the associated estimations of the future system behavior, might be subsequently used to optimize the maintenance strategy and the diagnosis schedule.