Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery.Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio,image,and video.However,these me...Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery.Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio,image,and video.However,these methods falter when confronting non-Euclidean datasets,notably graph representations.In response,here we introduce an innovative approach harnessing the graph convolutional network(GCN)to analyze graph data derived from vibration signals related to bearing faults.This enhances the precision and reliability of fault diagnosis.Our methodology initiates by deriving a periodogram from the unprocessed vibration signals.Subsequently,this periodogram is mapped into a graph format,upon which the GCN is engaged for classification purposes.We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets.Within these experiments,an accelerometer chronicles vibration signals across varying load conditions.We probe into the diagnostic accuracy rates across diverse loads and signal-to-noise ratios.Furthermore,a comparative evaluation of our method against several established algorithms delineated in this study is undertaken.Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.展开更多
文摘Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery.Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio,image,and video.However,these methods falter when confronting non-Euclidean datasets,notably graph representations.In response,here we introduce an innovative approach harnessing the graph convolutional network(GCN)to analyze graph data derived from vibration signals related to bearing faults.This enhances the precision and reliability of fault diagnosis.Our methodology initiates by deriving a periodogram from the unprocessed vibration signals.Subsequently,this periodogram is mapped into a graph format,upon which the GCN is engaged for classification purposes.We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets.Within these experiments,an accelerometer chronicles vibration signals across varying load conditions.We probe into the diagnostic accuracy rates across diverse loads and signal-to-noise ratios.Furthermore,a comparative evaluation of our method against several established algorithms delineated in this study is undertaken.Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.
