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 g...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.展开更多
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)+2 种基金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)。
文摘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.
