中国机械工程2025，Vol.36Issue(9)：1905-1915,11.DOI:10.3969/j.issn.1004-132X.2025.09.001
基于多维复向特征融合与CNN-GRU的转子不平衡量识别方法
Rotor Unbalance Recognition Based on Multidimensional Complex Feature Fusion and CNN-GRU
摘要
Abstract
The existing unbalance identification algorithm without trial weight adopted an optimization algorithm framework and approximated the optimal solution through numerous iterative operations.How-ever,such strategies typically faced the limitations of slow convergence speed and the tendency to fall into local extrema.Therefore,neural networks were used to directly learn and analyze the complex mapping re-lationship between unbalance vibration response and unbalance,thus realizing high-precision unbalance identification.A sufficient unbalance vibration dataset with labels was constructed by simulating the rotor dynamics model.A feature fusion mechanism was designed to address the multi-dimensional complex-valued characteristics of unbalanced data.At the core algorithm level,a CNN-GRU hybrid model was con-structed.In this model,CNN was responsible for extracting local spatial features from vibration data,while GRU captured temporal dependencies within the vibration data.By integrating information from both spatial and temporal domains,the model's generalization ability and recognition accuracy were significantly enhanced.The unbalance recognition results of test set data and experimental bench demonstrate that this method may accurately predict the unbalance of the rotors,providing a rapid and accurate guide for dynamic balancing in the field without trial weights.关键词
转子/无试重/不平衡量识别/卷积神经网络-门控循环单元/多维复向特征融合Key words
rotor/without trial weight/unbalance identification/convolutional neural network-gated recurrent unit(CNN-GRU)/multidimensional complex feature fusion分类
机械制造引用本文复制引用
王坚坚,廖与禾,杨磊,薛久涛..基于多维复向特征融合与CNN-GRU的转子不平衡量识别方法[J].中国机械工程,2025,36(9):1905-1915,11.基金项目
国家重点研发计划(2019YFB1311903) （2019YFB1311903）
国家自然科学基金(51575424) （51575424）
