Citation

Bustos A, Rubio H, Castejón C, García-Prada JC. Sensors (Basel) 2018; 18(3): s18030793.

Affiliation

MAQLAB Research Group, Department of Mechanical Engineering, Universidad Carlos III de Madrid, Av. de la Universidad, 30, 28911 Leganes (Madrid), Spain. jcgprada@ing.uc3m.es.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18030793

PMID

29509690

Abstract

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.

Language: en

Keywords

EMD; condition monitoring; empirical mode decomposition; high-speed train; maintenance; time evolution of spectral power; vibratory analysis