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

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.