Fault Diagnosis of Linear Guide Rails Based on SSTG Combined with CA-DenseNet  

作　　者：Yanping Wu Juncai Song Xianhong Wu Xiaoxian Wang Siliang Lu 

机构地区：[1]School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China [2]School of Internet,Anhui University,Hefei 230039,China [3]School of Electronic Information Engineering,Anhui University,Hefei 230601,China

出　　处：《Journal of Dynamics, Monitoring and Diagnostics》2024年第1期1-10,共10页动力学、监测与诊断学报（英文）

基　　金：supported by the following organizations:National Natural Science Foundation of China(Grant Nos.52375522,52207036,and 62203010);the Anhui Provincial Nat-ural Science Foundation(Grant Nos.2308085Y03 and 2208085QE167);the Project of the Outstanding Young Talents in Colleges and Universities of Anhui Province(Grant No.gxyqZD2022006);the College Natural Science Research Key project of Anhui Education Department(Grant No.KJ2021A0018);the University Outstanding Youth Research Project of Anhui Province(Grant No.2022AH030016)。

摘　　要：Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production.Thus,this paper proposes a new method of conducting the fault diagnosis of linear guide rails.First,synchrosqueezing transform(SST)combined with Gaussian high-pass filter,termed as SSTG,is proposed to process vibration signals of linear guide rails and obtain time-frequency images,thus helping realize fault feature visual enhancement.Next,the coordinate attention(CA)mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework,thus realizing accurate fault classifica-tion.Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%.The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.

关 键 词：CA-DenseNet fault diagnosis linear guide rails SSTG 

分 类 号：O313[理学—一般力学与力学基础]