Mechanics & Industry
Volume 20, Number 1, 2019
|Number of page(s)||14|
|Published online||08 February 2019|
Fault detection of damper in railway vehicle suspension based on the cross-correlation analysis of bogie accelerations
Department of Railway Vehicles, University Politehnica of Bucharest, 313 Splaiul Independenţei, 060042 Bucharest, Romania
* e-mail: email@example.com
Accepted: 24 November 2018
Nowadays, the condition-based maintenance is associated more and more with railway transport to improve the safety, availability, reliability and capacity of this transport system, and to reduce life cycle costs for the railway vehicles. The condition-based maintenance requires that vehicle components are replaced based on their real condition, which implies the fault detection and isolation during the train's operation. The paper proposes a method to detect the failure of the damper in the primary suspension of the rail vehicle, based on the analysis of cross-correlation of the vertical accelerations measured on the bogie frame against the two axles. The numerical simulations and experimental results show a very good correlation between the bogie accelerations when the dampers are in a normal operation condition. This thing is shown based on the values of the cross-correlation coefficient (CCC) of the bogie accelerations. The failure in a damper can be detected by the decrease of the CCC of the bogie accelerations, a confirmed fact in the results derived from numerical simulations. The proposed method has more advantages, namely, it is a signal-based method and hence does not require a complex mathematical modelling of the vehicle-track system and knowledge of its parameters or of the external conditions; the method makes relative comparisons between measurements and hence reduces the effect of the factors that influence outputs; the method can be also extended for the secondary suspension; the method can be easily implemented on any type of bogie.
Key words: Railway vehicle / primary suspension / cross-correlation analysis / fault detection
© AFM, EDP Sciences 2019
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