The passenger access system (PAS) is a complex mechatronic train onboard module with high reliability and safety requirements. This module fulfills one of the dozen main onboard functions onboard train. Consequently, any related fault occurrence may have a serious impact on the safety and availability of the whole train operation. In this context, developing effective automated monitoring and diagnostic techniques for the PAS, as early as from the design phase of the system, becomes an essential and challenging task. In this paper, we carry out a monitoring study on this system, while considering a sufficiently high-level abstraction perspective that allows for adapting discrete event models representing the behavior of the system. First, we establish a Petri net behavioral model that includes the nominal operating mode as well as various faulty behaviors. Then, based on the established Petri net models, a fault detection approach is used to investigate the diagnosability property and synthesize the diagnosers regarding different predetermined classes of failures. Finally, we show how the outputs of the diagnosability analysis can help make efficient design choices that allow for improving the safety of the whole system.