Intelligent Fault Diagnosis Method for Rolling Bearing Based on SAMNV3
In order to accurately identify the fault categories of rolling bearings,which are essential components of mechanical equipment,this paper proposes a SAMNV3 intelligent fault diagnosis model for rolling bearings that integrates the self-attention(SA)mechanism and the lightweight network MobileNetV3.This model takes advantage of the adaptive weighting of the features by the self-attention mechanism and the small size of the lightweight network MobileNetV3 to achieve end-to-end rolling bearing intelligent fault diagnosis by directly inputting the original vibration signals from two different datasets into the SAMNV3 model for feature extraction and fault identification and classification.The results of the validation of the two different datasets show that the model has high accuracy and low computational complexity,which can effectively improve the accuracy and reliability of rolling bearing fault diagnosis.
Rolling bearingIntelligent fault diagnosisSelf-attention mechanismLightweight networkMobileNetV3
NETL
NSTL
万方数据
