Considering availability purposes for train transportation, passenger accesses (doors and steps) are often designated as critical systems. To improve global availability of its rolling stock, Bombardier Transportation (BT) aims at reinforcing its maintenance procedure by introducing predictive diagnosis. The SURFER project has been initiated to develop online and in-cars tools to early detect and prevent faults. In this paper, an overview of achieved progress with respect to online predictive diagnosis will be introduced. For this purpose, many signals are recorded using a test bench by BT: electrical motor intensity current, door displacement, binary indicators as door closed and locked. The paper focuses on designing a semi-Supervised discriminative probabilistic model that take into account contextual variables (train inclination or constraints due to passengers affluence) to perform a robust predictive diagnosis. The main steps of the proposed method are the followings: the segmentation of the provided signals into opening and closing phases, the extraction of relevant features from opening/closing phases, the setting of the discriminative diagnosis model based on statistical semi-supervised learning. The proposed approach is tested on signals collected from regional trains fleeting around Paris. It allows the earlier detection of anomalies, for instance, those due to maladjustments. The practical implementation of this approach will be detailed together with its preliminary results.