佛山科学技术学院学报（自然科学版）2025，Vol.43Issue(5)：1-8,8.
基于改进时序网络的电机轴承故障诊断方法
Improved temporal network-based method for motor bearing fault diagnosis
摘要
Abstract
Fatigue wear of the bearings in high-power variable-frequency motors causes the vibration signals to be mixed.In order to improve the accuracy of fault diagnosis,a multi-channel insulated bearing time information fusion diagnosis model was designed,and the coarse-grained features with temporal patterns were extracted from the measured fault data.The self-attention mechanism was introduced into the designed time information fusion diagnosis model for optimization,and the correlation weights between the data were calculated by constantly updating the weight coefficients.The proposed diagnostic framework demonstrated superior overall fault recognition rate of 99.1%,significantly outperforming conventional recurrent neural network architectures including GRU(94.7%),LSTM(91.2%),and RNN(88.4%).关键词
电机绝缘轴承/故障诊断/改进时序网络/自注意力机制Key words
motor insulated bearings/fault diagnosis/improved timing network/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
黄星源,杨同光,蒋玲莉,李学军..基于改进时序网络的电机轴承故障诊断方法[J].佛山科学技术学院学报（自然科学版）,2025,43(5):1-8,8.基金项目
广东省基础与应用基础研究基金项目(2023A1515240083,2023A1515140029) （2023A1515240083,2023A1515140029）
