华东交通大学学报2025，Vol.42Issue(3)：96-107,12.
基于1DResAE网络模型的车轮多边形检测研究
Research on Wheel Polygon Detection Based on 1DResAE Network Model
摘要
Abstract
Deep learning technology offers advantages in vibration signal recognition with high accuracy and precision.However,acquiring a large number of labeled data for polygonal wheel detection is challenging,which fails to meet the training requirements of conventional neural network models.Existing methods to ad-dress the issue of small sample sizes often convert time-domain data into frequency-domain data,but this can re-sult in the loss of certain data features during the time-frequency conversion.To address this issue,a polygonal wheel detection method based on the 1DResAE deep neural network model is proposed.This model completes the detection of polygonal train wheels by unsupervised learning,feature extraction,and supervised learning of time-domain signals without the need for time-frequency conversion of vibration signals.By integrating one-di-mensional convolution,residual networks,and autoencoders,a one-dimensional deep neural network is formed,capable of extracting and learning complex one-dimensional vibration signal features.Based on the features ex-tracted and learned by the encoder in the autoencoder,the classifier performs supervised learning with a small amount of labeled data to achieve pattern recognition of polygonal train wheels.Experimental verification using data collected from a small-scale wheel-rail rolling test bench demonstrated that the detection accuracy of this method is 98.971%,with low error and outstanding classification performance.For the task of polygonal wheel detection,the 1DResAE model effectively detects the polygonal order of wheels and has practical applicability.关键词
车轮多边形/深度学习/故障检测/残差网络/自编码器Key words
wheel polygon/deep learning/fault detection/residual network/autoencoder分类
交通工程引用本文复制引用
林凤涛,倪鹏辉,杜磊,杨洋,杨世德,胡伟豪,谭荣凯..基于1DResAE网络模型的车轮多边形检测研究[J].华东交通大学学报,2025,42(3):96-107,12.基金项目
国家自然科学基金项目(52065021) （52065021）
江西省"双千计划"科技创新领军人才项目(S2021GDKX1442) （S2021GDKX1442）
江西省教育厅科学技术研究项目(GJJ210641) （GJJ210641）
华东交通大学载运工具与装备教育部重点实验室自主课题(KLCEZ2022-10) （KLCEZ2022-10）
中国国家铁路集团有限公司科技开发重点项目(N2023G021) （N2023G021）
