红外技术2026，Vol.48Issue(4)：508-515,8.
列车轴箱轴承红外图像智能检测与故障识别
Intelligent Infrared Imaging Inspection and Fault Detection of Train Axle Box Bearings
摘要
Abstract
Infrared thermal imaging technology,when combined with image processing techniques,enables long-distance,non-destructive,high-precision,and intelligent fault diagnosis and condition monitoring.Its application to the fault diagnosis and condition identification of axle box bearings—components with a high train failure rate—is therefore of significant practical importance.In this study,a train infrared thermal image dataset is utilized to train,validate,and test the single-stage deep learning object detection model YOLOv8,which incorporates a Convolutional Neural Network(CNN),for axle box detection.Subsequently,infrared thermal images corresponding to different bearing fault conditions of four axle boxes are simulated using COMSOL finite element analysis to generate an additional dataset.Three classification approaches—Bag of Visual Words(BoVW)with HOG Characteristic Gradient extraction,Support Vector Machine(SVM),and YOLOv8-based classification—are then employed for image classification and fault recognition.The results show that the YOLOv8-based classification model,which integrates a convolutional neural network for deep learning-based object detection,achieves the highest classification accuracy,reaching 100%.In comparison,the BoVW model attains an accuracy of up to 99.39%.In contrast,the combination of HOG features with SVM demonstrates relatively poor performance,with a classification accuracy of only 37.00%.关键词
列车轴箱/红外图像/视觉词袋/CNN/Hog特征与SVMKey words
train axlebox/infrared thermal image/visual bag of words/CNN/Hog features and SVM分类
信息技术与安全科学引用本文复制引用
王建鑫,郭佑民,杨君,赵鸿亮..列车轴箱轴承红外图像智能检测与故障识别[J].红外技术,2026,48(4):508-515,8.基金项目
国家自然科学基金资助项目(72061021). （72061021）
