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 crosscorrelation 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.

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

“…The current detection methods are based on acceleration measurements. The detection performance could be interfered by alignment, particularly when detecting small amplitude hunting instability [14]. Monitoring of LDWR can fundamentally solve this problem.…”

“…If the lateral displacement can satisfy a specific condition, the wheelset will be in pure rolling condition, resulting in minimal wear in a curve. Within the control chain, the measured LDWR provides feedback to the control system [14].…”

Modal parameters-based hunting stability analysis of high-speed railway vehicles considering full range of equivalent conicity

“…23,37 Two types of wheel/rail contact condition whose equivalent conicities are 0.08 and 0.4, are exploited here to reproduce the carbody hunting instability and bogie hunting instability, respectively. The main parameters of the railway vehicle used in the simulation are displayed in Appendix C. Part of the parameters (mainly the inertia parameters and structure parameters) are obtained from Ref 5 while the rest parameters (mainly the suspension parameters) are variable to perform optimization and the initial values are taken from the design values of one type EMU. Figure 5 shows the root locus diagrams for the vehicle system with the speed parametric excitation ranging from 10 km/h to 400 km/h in presence of these two equivalent conicities.…”

“…The wheel/rail contact geometry, regarded as one of the main causes of hunting instability, has a significant influence on the hunting stability of railway vehicles. [3][4][5] The equivalent conicity, a linearized parameter originating from the wheel/rail contact system, has been proposed and widely used in the railway community around the world to characterize and evaluate the wheel/rail contact conditions. 6,7 Consequently, the hunting stability of railway vehicles is closely related to the equivalent conicity of the equipped wheelsets, and researchers have conducted extensive studies on this subject from linear and nonlinear aspects.…”

Vehicle running instability detection algorithm (VRIDA): A signal based onboard diagnostic method for detecting hunting instability of rail vehicles

“…Zeng et al 14 proposed an data-driven algorithm to estimate state variable's periodicity in the non-linear dynamic system to detect hunting based on axlebox accelerations and described a corresponding hunting alarm strategy. Recently, Ye et al 15 proposed data fusion and data driven method to detect vehicle hunting more specifically, small-amplitude hunting occurrences whereas, Sun et al 16 proposed a cross-correlation based method to detect small-amplitude hunting occurrences. The above methods are classified into OMA based and data-driven methods and both kinds of methods require a lot of computer calculations either in time domain or in frequency domain for identification of vehicle instability.…”