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The condition monitoring of railway vehicles and of the infrastructure (track and overhead equipment) through the use of vehicle-based sensors is becoming a major trend for the railway industry and, to this aim, new rolling stock generations are often natively fitted with numerous sensors. However, older vehicles are not equipped with sensors and monitoring systems so that, considering the long service life of the rolling stock, advanced monitoring and condition-based maintenance techniques can only be applied to a relatively minor portion of the in-service railway fleets. This paper presents results from a research programme aimed at designing and testing an innovative monitoring system based on wireless sensor nodes, suitable for the retrofitting of older rolling stock generations, not natively equipped with advanced sensing and monitoring capabilities. The prototype monitoring system was used to detect the incipient bogie instability, which can be related to degradation in some suspension components or to increase of the equivalent conicity at wheel/rail contact. Different vehicle conditions and the influence of the track were considered in the study. Experimental tests have shown that is possible to identify not only how the vehicle condition is evolving in time but also the effect that maintenance operations like wheel reprofiling may have on the bogie lateral dynamics.
Reyes et al. (Mon,) studied this question.