Fault Diagnosis Method of Rolling Bearing Based on Mixed Attention Mechanism
Aiming at the problem of weak adaptive extraction ability and low fault diagnosis rate of ordinary intelligent fault diagno-sis method of rolling bearing,a fault diagnosis method of rolling bearing based on mixed attention mechanism model was proposed.The original 1D vibration signal was converted into a 2D feature image by continuous wavelet transform,which was input into the convolu-tional block attention module to adaptively extract fault features.The extracted feature images were input into the TDSC model to quantify the model parameters,reduce the memory occupied by each parameter,compress the trained complex model,and improve the model rea-soning speed and model training accuracy.Finally,two different public bearing data sets were used for experimental verification.The re-sults show that the highest fault diagnosis accuracy of two data sets reaches 99.99％and 99.70％,respectively.The feasibility and supe-riority of the bearing fault diagnosis method based on mixed attention mechanism are proved.
deep learningmixed attention mechanismconvolutional neural networkrolling bearingfault diagnosis
NETL
NSTL
万方数据
