A signal analysis based hunting instability detection methodology for high-speed railway vehicles

Abstract

Hunting stability is a long-standing research topic and has been deeply investigated due to its great influence on railway vehicle dynamic performances. Most of the existing hunting monitoring methods detect only the large amplitude hunting instability (LAHI). However, the small amplitude hunting instability (SAHI) is still hard to be detected accurately and efficiently. To face this challenging problem, this paper describes a signal analysis based hunting instability detection methodology. The proposed method is based on cross-correlation techniques and is able to detect both SAHI and LAHI in a simple, efficient and effective way. Eight cross-correlation indicators (CCIs) are exploited to detect anomalous SAHI and LAHI conditions. A fully detailed dynamic model of one typical high-speed railway vehicle is developed to test the methodology and to compare the CCIs under different vehicle operating conditions. The most effective CCI and its critical values are determined on the basis of the statistics and comparisons of the simulation results. Furthermore, the robustness of the proposed method to distinguish hunting instability and periodic excitations coming from track irregularities has been verified. Finally, the proposed instability detection methodology has been validated by detecting the SAHI successfully on field test data coming from specific experimental campaigns.

KEYWORDS:

• High-speed railway vehicles
• small amplitude hunting instability
• fault detection
• cross-correlation analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has been supported by the Independent Subject of State Key Laboratory of Traction Power [grant number 2018TPL_T04], the National Key R&D Program Subsidized Project [grant number 2018YFB1201700], and by the National Science Foundation for Young Scholars [grant number 51805450].