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.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).