用被动超声波+机器学习，实现铁路车轮缺陷的非接触自动识别。
Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects
- 通过统计分析筛选关键声学特征，结合随机森林分类器进行多类缺陷识别。
- 在9种车轮状态上达到0.66平衡准确率和0.65宏F1值。
- 方法可压缩特征集，适合部署到现场检测系统，提升维护效率。
可靠的铁路车轮缺陷识别对安全与维护至关重要。本研究基于机器学习构建了一套诊断框架，利用被动空气耦合超声声发射信号实现多类缺陷识别。数据来自11组全尺寸车轮组，涵盖9种健康状态。通过克鲁斯卡尔-沃利斯检验和互信息分析，评估时域与频域特征的判别能力。选用关键特征训练随机森林分类器，并采用分层5折交叉验证。模型在9个类别上实现约0.66的平衡准确率和0.65的宏F1分数。衰减率、峭度、偏度及包络低频能量被识别为最具影响力的特征，且精简特征子集仍保持主要分类性能。结果表明，结合被动超声传感、统计特征选择与监督学习，可实现非接触式铁路车轮缺陷分类，为未来可部署的检测系统奠定基础。
原文摘要 · Abstract (English)
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 acoustic emission signals. Data were collected from eleven full-scale railway wheelsets representing nine health states. Time- and frequency-domain features were evaluated using Kruskal-Wallis statistical testing and mutual-information analysis to identify the most discriminative indicators. A Random Forest classifier was then trained using the selected features with stratified 5-fold cross-validation. The model achieved a balanced accuracy of approximately 0.66 and a Macro-F1 score of 0.65 across the nine classes. Decay rate, kurtosis, skewness, and envelope low-frequency power emerged as the most influential features, while a compact subset of features retained most of the classification performance. The results demonstrate the feasibility of combining passive ultrasonic sensing, statistical feature selection, and supervised machine learning for non-contact railway wheel defect classification and provide a foundation for future field-deployable inspection systems.
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