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.

狩猎稳定性是一个长期存在的研究课题，由于其对轨道车辆动态性能的影响很大，因此得到了深入的研究。大多数现有的振荡监测方法仅检测大振幅振荡不稳定性（LAHI）。然而，小幅度振荡不稳定性（SAHI）仍然难以准确有效地检测。为了面对这个具有挑战性的问题，本文描述了一种基于信号分析的狩猎不稳定性检测方法。所提出的方法基于互相关技术，能够以简单、高效和有效的方式检测 SAHI 和 LAHI。利用八个互相关指标 (CCI) 来检测异常 SAHI 和 LAHI 条件。开发了一种典型高速铁路车辆的完全详细的动态模型，以测试该方法并比较不同车辆运行条件下的 CCI。最有效的 CCI 及其临界值是根据模拟结果的统计和比较确定的。此外，所提出的用于区分来自轨道不规则性的狩猎不稳定性和周期性激励的方法的鲁棒性已经得到验证。最后，通过在来自特定实验活动的现场测试数据上成功检测 SAHI，验证了所提出的不稳定性检测方法。此外，所提出的用于区分来自轨道不规则性的狩猎不稳定性和周期性激励的方法的鲁棒性已经得到验证。最后，通过在来自特定实验活动的现场测试数据上成功检测 SAHI，验证了所提出的不稳定性检测方法。此外，所提出的用于区分来自轨道不规则性的狩猎不稳定性和周期性激励的方法的鲁棒性已经得到验证。最后，通过在来自特定实验活动的现场测试数据上成功检测 SAHI，验证了所提出的不稳定性检测方法。