机械科学与技术2025，Vol.44Issue(5)：751-758,8.DOI:10.13433/j.cnki.1003-8728.20230206
残差网络在滚动轴承故障损伤尺寸识别中的应用
Application of Residual Network in Dimension Identification of Rolling Bearing Fault Damage
摘要
Abstract
In order to solve the problem of low accuracy of damage size identification of rolling bearing based on machine learning,a method of fault damage size identification of rolling bearing based on deep residual network is proposed.This method takes residual network as the main frame of deep feature extraction,and establishes a network model that can map the preprocessed vibration sample data to the corresponding damage size.Fourier transform is used in data preprocessing to obtain the spectrum of the original vibration acceleration time-domain signal as the input of the network.The proposed damage size identification method is verified by different damage size tests on two types of testers such as rolling bearing accelerated fatigue tester and aero-engine rotor tester,and compared with other methods.The results show that,within the prediction error range of 0.3 mm,the recognition accuracy of the network model for the fault damage size without training is 91.2％ for the inner ring and 97.9％ for the outer ring.At the same time,the prediction error of the damage size can still reach the recognition accuracy within 0.3 mm after the data is processed with noise.The results fully show that this method has a strong ability to identify fault damage size.关键词
深度学习/残差网络/傅里叶变换/滚动轴承/损伤尺寸识别Key words
deep learning/residual network/fast fourier transform(FFT)/rolling bearing/damage size identification分类
金属材料引用本文复制引用
吴英祥,杜少辉,赵紫豪,尉询楷,陈智超,陈果..残差网络在滚动轴承故障损伤尺寸识别中的应用[J].机械科学与技术,2025,44(5):751-758,8.基金项目
国家自然科学基金项目(52272436)、国家科技重大专项(J2019-IV-0004-0071)及中国航发沈阳发动机研究所项目 （52272436）
