报告详情
A Twin Model Construction Method for Unbalanced Vibration in Rotor Systems Based on Random Forest
编号：46
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更新：2025-06-15 10:42:26 浏览：16次
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摘要
Aiming at the problems of insufficient real-time monitoring of the dynamic characteristics of rotating machinery under multiple working conditions and high data acquisition costs, A twin model construction method for unbalanced vibration in rotor systems based on random forests is proposed in this paper to achieve rapid prediction of the unbalance or vibration response. Based on the finite element dynamic model, the characteristic data are obtained by applying unbalanced excitation through harmonic response analysis. On the basis of considering the dynamic characteristics of the rotor system, the key node features are extracted based on K-means clustering sampling. According to the requirements of different application scenarios, a three-level prediction twin model is constructed based on the dynamic characteristics of key nodes after order reduction: Model Ⅰ for predicting unbalance, Model Ⅱ for predicting both unbalance and rotational frequency simultaneously, and Model Ⅲ for predicting the frequency response function covering the entire frequency band. Taking the finite element model of a certain rotor system as an example for verification, the results show that the root mean square error (RMSE) of Model Ⅰ in the unbalance prediction is 0.1726 g·m, and the mean absolute error (MAE) is 0.15 g·m. In the rotational frequency prediction of Model Ⅱ, the error does not exceed 0.9 Hz. The RMSE of the unbalance prediction is 0.1504 g·m, and the MAE is 0.116 g·m. In the frequency response function prediction of Model Ⅲ, the Pearson correlation coefficients of each measurement point are all higher than 0.98. The validation results demonstrate that the proposed method offers excellent prediction accuracy and practical engineering value. Real-time inversion of unbalance, rapid and accurate prediction of rotational speed and unbalance in the keyless phase, and overall dynamic balance within the working rotational speed range can be achieved through this method.
关键词
rotor unbalance,dynamic twin model,K-means clustering,finite element analysis,random forest
报告人
Xiang Li
PhD Student Beijing University of Chemical Technology稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
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2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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