Abstract The in-service safety of railway axles is a very important engineering challenge, as it has a large impact not only from the economic point of view of the railway operator, but it has cascading effects on supply chains, loss of work productivity, and, in the most serious cases, loss of life. It is, therefore, vital that the structural integrity of such components is known, during their lifecycle, with the highest possible accuracy via precise modelling, reliable inspections and, more recently but still at research level, effective condition monitoring. With a focus on solid freight axles, the research investigates the applicability of Acoustic Emission as a structural health monitoring approach for determining the in-service condition of a full-scale axle. A fatigue crack propagation test is carried out in the lab subjecting the axle to many repetitions of a block load sequence defined from real service measurements. Acoustic Emission data are continuously recorded during the test, whilst crack size is periodically measured by conventional non-destructive techniques. Eventually, a first-approximation correlation is highlighted between Acoustic Emission data, post-processed by a machine-learning algorithm, and crack propagation ones.

中文翻译：

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一种基于声发射的结构健康监测方法，用于固体铁路车轴的损伤发展

摘要 铁路车轴在役安全是一项非常重要的工程挑战，因为它不仅从铁路运营商的经济角度产生巨大影响，而且对供应链、工作生产力损失和，在最严重的情况下，会失去生命。因此，至关重要的是，在其生命周期内，通过精确建模、可靠检查以及最近但仍处于研究水平的有效状态监测，以尽可能高的精度了解此类组件的结构完整性。该研究以实心货运车桥为重点，调查了声发射作为一种结构健康监测方法的适用性，以确定全尺寸车桥的使用状况。疲劳裂纹扩展测试在实验室中进行，使车轴多次重复根据实际使用测量定义的块载荷序列。在测试过程中连续记录声发射数据，同时通过传统的非破坏性技术定期测量裂纹尺寸。最终，通过机器学习算法进行后处理的声发射数据与裂纹扩展数据之间的一阶近似相关性被突出显示。