排灌机械工程学报2026，Vol.44Issue(8)：827-837,11.DOI:10.3969/j.issn.1674-8530.25.0102
基于辛几何模态分解与深度学习融合的推力轴承瓦温预测
Thrust bearing bush temperature prediction based on fusion of symplectic geometric mode decomposition and deep learning
摘要
Abstract
To address the difficulty in accurately predicting the temperature of the thrust bearing guide pad of hydroelectric generator units due to the coupling and nonlinear effects of multiple factors,a combined prediction model based on SGMD-SE-AVOA-BiLSTM-Informer was proposed by using in-depth mechanism analysis to screen out key influencing factors.First,the temperature signal was de-composed into multiple scales and reconstructed into single components using symplectic geometric mode decomposition(SGMD)combined with sample entropy(SE).Then,for components with diffe-rent complexities,bidirectional long short-term memory network(BiLSTM)and Informer model were used for collaborative modeling.The key hyperparameters were optimized using the African vultures op-timization algorithm(AVOA).Finally,the prediction results of each component were integrated to achieve the accurate prediction of temperature.The experimental results show that the prediction indices RMSE,MAE,MAPE and R2 of the proposed model are 0.516 1℃,0.418 1℃,1.031 9%and 0.997 5,respectively,which show significant advantages over the two single models and the five coupled ablation models.This effectively verifies the applicability and superiority of the method in pro-cessing the multi-scale and nonlinear characteristics of the temperature signal of the thrust bearing guide pad.关键词
推力轴承/瓦温预测/辛几何模态分解/样本熵/非洲秃鹫优化算法/双向长短期记忆网络/InformerKey words
thrust bearing/bush temperature prediction/symplectic geometric mode decomposition/sample entropy/African vultures optimization algorithm/BiLSTM/Informer分类
建筑与水利引用本文复制引用
李佰霖,张钰烽,唐淞,马云帆..基于辛几何模态分解与深度学习融合的推力轴承瓦温预测[J].排灌机械工程学报,2026,44(8):827-837,11.基金项目
梯级水电站运行与控制湖北省重点实验室开放基金项目(2021KJX04) （2021KJX04）
