Degradation processes modelling is a key problem to perform any type of reliability study. Indeed, the quality of the computed reliability indicators and prognosis estimations directly depends on this modelling. Mathematical models commonly used in reliability (Markov chains, Gamma processes...) are based on some assumptions that can lead to a loss of information on the degradation dynamic. In many studies, Dynamic Bayesian Networks (DBN) have been proved relevant to represent multicomponent complex systems and to perform reliability studies. In a previous paper, we introduced a, degradation model based on DBN named graphical duration model (GDM) in order to represent a wide range of duration models. This paper will introduce a new degradation model based on GDM integrating the concept of conditional sojourn time distributions in order to improve the degradation modelling. It integrates the possibility to take into account several degradation modes together and to adapt the degradation modelling in respect of some new available observations of either the current operation state or the estimated degradation level, to take into account an eventual dynamic change. A comparative study on simulated data between the presented model and the GDM will be performed to show the interest of this new approach.