Time–frequency characteristic analysis method for track geometry irregularities based on multivariate empirical mode decomposition and Hilbert spectral analysis

DOI number:10.1080/00423114.2019.1711137

Affiliation of Author(s):地球科学与环境工程学院

Teaching and Research Group:测绘遥感信息系

Journal:Vehicle System Dynamics

Place of Publication:英国

Key Words:Track geometry irregularities; track inspection vehicle; time–frequency energy density; multivariate empirical mode decomposition; Hilbert spectral analysis

Abstract:The description and evaluation of track geometry irregularities are commonly performed in the distance domain or the frequency domain, and therefore one cannot simultaneously obtain precise measurements of actual frequencies and the distance locations where they appear. This paper proposes the time–frequency energy density (TFED) method to analyse the characteristics of track irregularities in the joint spatial distance–frequency plane. The proposed method describes the energy density distribution of track irregularities in the spatial distance–frequency domain based on the adaptive decomposition characteristics of the multivariate empirical mode decomposition and the time–frequency aggregation on the signal energy of the Hilbert spectrum. The mathematical relationships between the TFED and the standard deviation, as well as the power spectral density, are deduced and validated in the field data of track irregularities. The TFED method was applied to the spatial distance–frequency characteristic distribution of track irregularities and the distribution of instantaneous energy density in the sensitive wavelengths. The track geometry quality is evaluated based on the TFED, using track inspection data of actual high-speed railways.

Indexed by:Journal articles

Document Code:1711137

Discipline:Engineering

Document Type:J

Volume:59

Issue:5

Page Number:719-742

ISSN No.:https://www.tandfonline.com/loi/nvsd20

Translation or Not:no

Date of Publication:2020-05-01

Included Journals:SCI

Links to published journals:https://doi.org/10.1080/00423114.2019.1711137