机械科学与技术2024，Vol.43Issue(5)：790-797,8.DOI:10.13433/j.cnki.1003-8728.20230054
小波变换和深度残差收缩网络在齿轮箱故障诊断中的应用
Application of Wavelet Transform and Deep Residual Shrinkage Network in Gearbox Fault Diagnosis
摘要
Abstract
Accurate fault diagnosis of gears is an effective means to ensure stable and reliable operation of rotating machinery.Aiming at the problem of gear fault classification in gearboxes under strong noise environment,a fault diagnosis model based on continuous wavelet transform and deep residual shrinkage network is proposed.Firstly,wavelet transform is used to analyze the vibration data of one-dimensional time series,and it is converted into a two-dimensional time-frequency map as the input of the deep residual shrinkage network(DRSN).Secondly,based on the multi-layer convolutional neural network,the cross-layer identity connection in the residual structure is added to solve the problem of gradient disappearance and explosion,and then the adaptive threshold sub-network is used to achieve soft threshold noise reduction.Finally,the time-frequency map of the fault sample is used as the input of the diagnosis model to achieve fault classification.The experimental results show that compared with other models,the fault diagnosis method is easier to identify fault features,and the classification accuracy rate reaches 99.15%.关键词
齿轮箱/时频分析/深度残差收缩网络(DRSN)/故障诊断Key words
gearbox/time-frequency analysis/DRSN/fault diagnosis分类
机械制造引用本文复制引用
翁敏超,王海瑞,朱贵富..小波变换和深度残差收缩网络在齿轮箱故障诊断中的应用[J].机械科学与技术,2024,43(5):790-797,8.基金项目
国家自然科学基金项目(61863016,61263023) （61863016,61263023）
