In-service structural health monitoring is a so far rarely exploited, yet potent option for early-stage crack detection and identification in train wheelset axles. This procedure is non-trivial to enforce on the basis of a purely data-driven approach and typically requires the adoption of numerical, e.g. finite element-based, simulation schemes of the dynamic behavior of these axles. Damage in this particular case can be formulated as a breathing crack problem, which further complicates simulation by introducing response-dependent nonlinearities into the picture. In this study, first, a new crack detection feature based on higher-order harmonics of the breathing crack is proposed, termed Higher-Order Transmissibility (HOTr), and, secondly, its sensitivity and efficacy are assessed within the context of crack identification. Next, the mentioned feature is approximated via use of linear system theory, delivering a surrogate model which facilitates the computation and speeds up the crack identification procedure. The accuracy of the proposed method in reproducing the delivered HOTr is compared against the nonlinear simulation model. The obtained results suggest that the approximation of the HOTr can significantly reduce the computational burden by eliminating the need for an iterative solution of the governing nonlinear equation of motion, while maintaining a high level of accuracy when compared against the reference model. This implies great potential for adoption in in-service damage identification for wheelset axles, feasibly within a near real-time context.

翻译：在役结构健康监测是列车轮对轴早期裂纹检测与识别中一种目前尚未充分开发但极具潜力的技术方案。基于纯数据驱动的方法实施该过程具有挑战性，通常需要采用数值模拟方案（例如基于有限元方法）来模拟这些车轴的动态行为。在此特定案例中，损伤可表述为呼吸裂纹问题，其通过引入响应依赖的非线性特性进一步增加了模拟的复杂性。本研究首先提出了一种基于呼吸裂纹高阶谐波的新型裂纹检测特征，称为高阶传递率；其次，在裂纹识别框架内评估了该特征的灵敏度与有效性。随后，通过线性系统理论对该特征进行近似处理，构建出替代模型以简化计算并加速裂纹识别流程。将所提方法在复现高阶传递率方面的精度与非线性仿真模型进行对比。结果表明：高阶传递率的近似方法无需迭代求解控制非线性运动方程，可显著降低计算负担，同时相较于参考模型仍能保持较高精度。这预示着该方法在轮对轴在役损伤识别领域（甚至可能在近实时场景中）具有巨大的应用潜力。