The predictive analysis of the operating state of complex dynamic systems remains a challenge in numerous applications, particularly in the transport field. Its main objective is the estimation and prediction of the state of health of these systems from data usually available in the form of multivariate time series and emanating from multiple sensors. A classic approach to tackle this problem consists in assuming that the state of health switches between a finite set of operating states. In this case, supervised and unsupervised classification methods can be exploited, as well as finite state space dynamic models (eg. hidden Markov models). This contribution addresses the same issue, but from a different point of view: the unknown dynamics of the state of health is searched into a continuous low-dimensional space. The resulting model is a dynamic factor analytic model which can be seen as a specific state-space models. It can also be exploited for visualizing the evolution of the system's operating state over time. This article will describe the implementation of this model for the estimation, forecasting and visualization of the state of health of a specific railway component: the switch mechanism.