CBM for a fleet of railway vehicles: infrastructure and algorithms





Data acquisition and communication technologies give the possibility of receiving and storing a huge amount of data from machinery and plants in operation. From these data it is possible to create a set of Key Maintenance Indicators (KMI) useful for optimizing the maintenance policy. Raw data from the field are to be processed and filtered for obtaining effective KMIs to use in algorithms aimed at discovering anomalies or abnormal operation of one or more machineries or plants.

This paper presents a roadmap towards the Condition Based Maintenance of a fleet of railway vehicles. The paper associates to each maintenance policy its benefits and its requirements in terms of technological infrastructure and operating costs. Bombardier Transportation Italy started this roadmap a few years ago, for moving from a reactive maintenance policy to a proactive policy.

Increasing the effectiveness of maintenance implies the sensorization of the machines and the creation of a network for funneling information from the machineries to the central maintenance room. A Company must find an equilibrium point between complexity and expected benefits.

Results achieved by means of a specifically developed tool for data analysis applied to some sub-systems of the vehicles are presented.

Author Biography

Paolo Pinceti, University of Genoa







Research Papers