基于多维复向特征融合与CNN-GRU的转子不平衡量识别方法

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中图分类号：TH17DOI：10.3969/j.issn.1004-132X.2025.09.001 开放科学（资源服务）标识码（OSID）：

Rotor Unbalance Recognition Based on Multidimensional Complex Feature Fusion and CNN-GRU

WANG Jianjian1.²LIAO Yuhe1.2* YANG Lei1² XUE Jiutao1，2

1.Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System， Xi'an Jiaotong University，Xi'an，710049

2.Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics， Xi'an Jiaotong University，Xi'an，710049

Abstract：The existing unbalance identification algorithm without trial weight adopted an optimization algorithm framework and approximated the optimal solution through numerous iterative operations.However，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 relationship between unbalance vibration response and unbalance，thus realizing high-precision unbalance identification.A suficient unbalance vibration dataset with labels was constructed by simulating the rotor dynamics model. A feature fusion mechanism was designed to address the multi-dimensional complexvalued characteristics of unbalanced data.At the core algorithm level，a CNN-GRU hybrid model was constructed.In this model，CNN was responsible for extracting local spatial features from vibration data， while GRU captured temporaldependencies within the vibration data.By integrating information from both spatial and temporaldomains，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.

Key words：rotor；without trial weight；unbalance identification；convolutional neural network-gated recurrent unit （CNN-GRU）；multidimensional complex feature fusion

0 引言

不平衡故障是旋转机械中最主要的振动问题之一[]，据统计， 50% 以上的系统故障和设备失灵与旋转机械故障有关，超过 80% 的旋转机械振动故障都可以追溯到转子不平衡问题[3]。（剩余15071字）