English(EN) Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects

机器学习框架利用超声信号检测铁路车轮缺陷

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员开发了一个机器学习框架，利用无损超声信号检测铁路车轮的缺陷。该系统分析轮对的声发射数据，识别时域和频域的关键特征。使用这些特征训练的随机森林分类器，在九种不同的缺陷类别上实现了约0.66的平衡准确率和0.65的宏F1分数，展示了非接触式检测系统的潜力。 AI

影响这项研究展示了机器学习在安全关键基础设施方面的新应用，有望提高检测效率和可靠性。

排序理由关于用于缺陷检测的新机器学习框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Aashish Shaju, Steve Southward, Mehdi Ahmadian · 2026-08-11 04:00

基于机器学习的被动超声波检测铁路车轮缺陷诊断框架

arXiv:2608.08301v1 Announce Type: new Abstract: Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framework for multi-class defect identification using passive air-coupled ultrasonic a…