河南理工大学学报（自然科学版）2024，Vol.43Issue(5)：108-117,10.DOI:10.16186/j.cnki.1673-9787.2022040075
基于数据映射和胶囊网络的轴承故障诊断方法
Bearing fault diagnosis method based on data mapping and CapsNet
摘要
Abstract
Conventional deep learning models adaptively extract fault features from vibration signals to realize end-to-end bearing fault diagnosis.However,the vibration monitoring signal is a very complex non-stationary time series signal,and if the deep network directly takes the original vibration signal as input,the nonlinear coupling effect between the data will greatly affect the efficiency of the model for fault feature extraction.Objectives To reduce the strong nonlinear coupling effect between fault signals,and to solving the problem of the convolutional neural network on the loss of spatial constraint information so as to im-prove the performance of bearing fault diagnosis,Methods a bearing fault diagnosis method based on data mapping and capsule network(CapsNet)was proposed.Firstly,the color space model(color names,CN),which could refine color features in the image processing field,was introduced into the fault data preprocess-ing to map the original low-dimensional space data to the high-dimensional space and improve the spatial differentiation of the fault data.Secondly,to address the problem of high dimensionality and redundancy of the mapped data that affected the efficiency of fault diagnosis,principal component analysis(PCA)was in-troduced to extract the main meta-information of the fault data,which reduced the dimensionality of the data.Finally,considering the ability of the capsule network to effectively extract spatial constraint information,CapsNet was used as the backbone network for fault diagnosis to identify and classify fault features.Results The method was validated using the Case Western Reserve University(CWRU)and Xi'an Jiaotong Univer-sity(XJTU-SY)bearing datasets,the experimental results showed that the method achieved a fault diagno-sis accuracy of more than 98%on both datasets,and the diagnostic performance of the method had certain advantages when compared with other deep learning-based fault diagnosis methods.Conclusions The pro-posed bearing fault diagnosis method could effectively decouple the fault data,improve the spatial differen-tiation between the data,and then obtain higher bearing fault diagnosis accuracy.关键词
轴承故障诊断/颜色空间模型/数据空间映射策略/主成分分析/胶囊网络Key words
bearing fault diagnosis/color names/data space mapping strategy/principal component analysis/capsule network分类
机械制造引用本文复制引用
赵运基,张楠楠,周梦林,许孝卓,张新良..基于数据映射和胶囊网络的轴承故障诊断方法[J].河南理工大学学报（自然科学版）,2024,43(5):108-117,10.基金项目
国家自然科学基金资助项目(61973105,61573130,52177039) （61973105,61573130,52177039）
河南省高校基本科研业务费项目(NSFRF200504) （NSFRF200504）
河南省科技攻关资助项目(212102210145,212102210197,222102220016) （212102210145,212102210197,222102220016）
