Abstract
<p>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.</p><p>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.</p><p>Increasing the effectiveness of maintenance implies the <em>sensorization</em> 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.</p><p>Results achieved by means of a specifically developed tool for data analysis applied to some sub-systems of the vehicles are presented.</p>
Highlights
Optimizing the maintenance for a fleet of machineries or vehicles means to guarantee a high Quality of Service (QoS) with a minimum number of interventions
We introduced the concept of Key Maintenance Indicator (KMI) that may represent:
Railway vehicles are composed by a set of independent subsystems, each one equipped with a dedicated Train Control & Management System (TCMS) that collects data both for control purposes and for diagnosis
Summary
Optimizing the maintenance for a fleet of machineries or vehicles means to guarantee a high Quality of Service (QoS) with a minimum number of interventions. It is mandatory to monitor the operation of a vehicle (or a machinery, or a plant) to find symptoms of incoming failures (“fix it before it fails”) For this purpose, various metrics and sensor-based methods can be used to measure and monitor continuously the condition of the ACTA IMEKO | www.imeko.org equipment. This paper shortly describes the on-board and off-board infrastructures, and focuses on the procedures for the data analysis and for the discovery of rules and metrics useful for CBM; in other words, the “Conditions” to active a maintenance intervention. The paper presents some results of this analysis for three different sub-systems
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