噪声与振动控制2025，Vol.45Issue(2)：63-69,7.DOI:10.3969/j.issn.1006-1355.2025.02.011
基于特征融合的齿轮箱故障诊断
Fault Diagnosis of Gearboxes Based on Feature Fusion
摘要
Abstract
The vibration signal of gearbox faults exhibits the characteristics of nonlinearity,non-stationarity and com-plexity of gearbox operating conditions,which leads to the difficulty for traditional signal processing methods to effectively extract gearbox fault characteristics and severe impacts on transmission accuracy and equipment operational safety.In view of this,in this paper,a method that combines dimensionless indicators of vibration signals with Empirical Mode Decomposi-tion(EMD)information entropy was proposed.This method utilized the Random Forest(RF)to compare and rank the impor-tance of different features and effectively overcome information redundancy.At the same time,the newly constructed sample set was used as input to train the Long Short-Term Memory(LSTM)neural network,and achieve the effective identification of various local faults in the gearbox.The effectiveness of the proposed method was validated using experimental data from Southeast University.A comparison with other methods demonstrates the computational efficiency and high identification ac-curacy of the proposed approach.This work contributes a new practical and methodological foundation for the intelligent di-agnosis of gearboxes.关键词
故障诊断/齿轮箱/融合特征/无量纲指标/随机森林/LSTM神经网络Key words
fault diagnosis/gearbox/fusion features/dimensionless indicators/random forest/LSTM neural network分类
机械制造引用本文复制引用
周芸,吴胜利,邢文婷..基于特征融合的齿轮箱故障诊断[J].噪声与振动控制,2025,45(2):63-69,7.基金项目
国家自然科学基金资助项目(51705052) （51705052）
国家社会科学基金资助项目(23BGL220) （23BGL220）
重庆市研究生联合培养基地建设资助项目(JDLHPYJD2020028) （JDLHPYJD2020028）
