AI 中文总结
本研究提出基于机器学习的铁路车轮缺陷被动超声检测框架，结合统计特征选择与随机森林分类器，实现9类车轮缺陷的非接触式识别，为现场检测系统奠定基础。
AI 中文摘要
可靠识别铁路车轮缺陷对安全与维护至关重要。本研究开发了一种基于机器学习的诊断框架，用于利用空气耦合被动超声声发射信号进行多类缺陷识别。数据采集自11个代表9种健康状态的全尺寸铁路轮对。通过Kruskal-Wallis统计检验和互信息分析评估时域与频域特征，以确定最具区分性的指标。随后使用选定特征训练随机森林（Random Forest）分类器，采用分层5折交叉验证。该模型在9个类别上实现了约0.66的平衡准确率和0.65的Macro-F1分数。衰减率、峰度、偏度及包络低频功率成为最具影响力的特征，而紧凑的特征子集保留了大部分分类性能。结果表明，将被动超声传感、统计特征选择与监督机器学习结合用于非接触式铁路车轮缺陷分类具有可行性，并为未来可现场部署的检测系统奠定了基础。
英文摘要
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.
CommentsPresented at the ASNT Research Symposium 2026, Salt Lake City, Utah, July 20-24, 2026
