电子科技2026，Vol.39Issue(9)：78-88,11.DOI:10.16180/j.cnki.issn1007-7820.2026.09.010
基于CEEMD-APSO-LSTM的步进电机故障诊断
Stepper Motor Fault Diagnosis Based on CEEMD-APSO-LSTM
摘要
Abstract
In view of the problem of reduced fault diagnosis accuracy caused by irrelevant data interference such as noise in the fault vibration signal of stepper motors,a fault diagnosis model for optimizing LSTM(Long Short-Term Memory Network)based on CEEMD(Complementary Ensemble Empirical Mode Decomposition)and APSO(Adaptive Particle Swarm Optimization)algorithm is proposed.To reduce the noise interference in one-di-mensional vibration data,the CEEMD(Complementary Ensemble Empirical Mode Decomposition)method is adopt-ed to decompose the data,and the most relevant fault features are extracted in combination with the Pearson correla-tion coefficient.Enhance the learning ability of the LSTM based on the attention mechanism,focus on key fault in-formation,and optimize the model parameters through the adaptive weight particle swarm optimization algorithm.The key fault components are input into the model,reconstructed and then output to achieve fault diagnosis.The ex-perimental results show that the accuracy rate of the CEEMD-APSO-LSTM fault diagnosis model is 99.6%,and the loss function value is 0.021 6.关键词
步进电机/故障诊断/互补集合经验模态分解/自适应权重粒子群算法/长短期记忆网络/注意力机制Key words
stepper motor/fault diagnosis/complementary ensemble empirical mode decomposition/adap-tive weighted particle swarm algorithm/long short-term memory network/attention mechanism分类
信息技术与安全科学引用本文复制引用
王锐,张建新,吕刚..基于CEEMD-APSO-LSTM的步进电机故障诊断[J].电子科技,2026,39(9):78-88,11.基金项目
浙江省科技厅重点研发项目(2022C01188)Key R&D Program of Zhejiang Science and Technology Department(2022C01188) （2022C01188）
