液压与气动2024，Vol.48Issue(7)：93-100,8.DOI:10.11832/j.issn.1000-4858.2024.07.011
麻雀搜索算法优化的外啮合齿轮泵泄漏量预测
Prediction of Leakage in External Gear Pump Optimized by Sparrow Search Algorithm
摘要
Abstract
Predicting the change trend of gear pump leakage can help quantitatively analyze its performance degradation process.The variational modal decomposition method is used to perform variational mode decomposition on the original leakage data of the gear pump,and the intrinsic mode function IMF is obtained.A model that combines sparrow search algorithm and long-short term memory is proposed.The VMD-SSA-LSTM model is established to predict the changes in gear pump leakage,and each component is predicted separately.Finally,the prediction results are superimposed to obtain the complete prediction results.By comparing the prediction results in different time periods,it can be seen that the VMD-SSA-LSTM model can reduce the average relative error of the prediction results by up to 25.2％compared with the single LSTM model,which can effectively predict the leakage quantity.The research conclusions can provide theoretical support for quantitative prediction of gear pump performance degradation.关键词
外啮合齿轮泵/泄漏量预测/变分模态分解/麻雀搜索算法/长短期记忆网络/性能退化Key words
external gear pump/leakage prediction/variational mode decomposition/sparrow search algorithm/long short-term memory network/performance degradation分类
机械制造引用本文复制引用
张立强,张建强,丁杰,李全军,李琛玺..麻雀搜索算法优化的外啮合齿轮泵泄漏量预测[J].液压与气动,2024,48(7):93-100,8.基金项目
国家自然科学基金(51565027) （51565027）
