文章摘要
赵洪利,魏凯.基于相似性与GA-RF的航空发动机剩余寿命预测[J].机床与液压,2022,50(12):167-173.
ZHAO Hongli,WEI Kai.Aeroengine Residual Life Prediction Based on Similarity and GA-RF[J].Machine Tool & Hydraulics,2022,50(12):167-173
基于相似性与GA-RF的航空发动机剩余寿命预测
Aeroengine Residual Life Prediction Based on Similarity and GA-RF
  
DOI:10.3969/j.issn.1001-3881.2022.12.031
中文关键词: 发动机寿命预测  健康指数  遗传算法  随机森林  多模型相似性
英文关键词: Aeroengine life predicttion  Health index  Genetic algorithm  Random forest  Multi model similarity
基金项目:
作者单位E-mail
赵洪利 中国民航大学航空工程学院 henleytrent@163.com 
魏凯 中国民航大学航空工程学院  
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中文摘要:
      针对单参数不能准确表征发动机性能退化过程,以及传统智能学习模型难以准确拟合发动机退化模型等问题,提出一种融合数据构建发动机健康指数(HI),并结合多模型相似性匹配与集成模型进行发动机剩余寿命预测的方法。利用层次聚类与轮廓系数筛选参数,并融合为发动机健康指数。采用遗传算法优化随机森林拟合发动机性能退化过程,并将多模型相似性匹配用于回归模型预测,优化模型的预测结果。选择某涡扇发动机仿真数据集(C-MPASS)验证所提方法的有效性。结果表明:该方法的RMSE为6.128、MAE为4.901,且融合健康指数和多模型相似匹配极大地提高了发动机剩余寿命预测精度。
英文摘要:
      The single parameter can not accurately represent the engine performance degradation process, and traditional intelligent learning model is difficult to accurately fit the engine degradation model. In order to solve the above problems,an engine health index (HI) based on data fusion was proposed, and the residual life prediction method was presented combining multi model similarity matching with integrated model. Hierarchical clustering and contour coefficient were used to select parameters, which were integrated into engine health index. The genetic algorithm was used to optimize the random forest to fit the engine performance degradation process, and the multi model similarity matching was used to optimize the prediction results of the regression model. A turbofan engine simulation data set (C-MPASS) were selected to verify the effectiveness of the proposed method. The results show that the RMSE and MAE of the method are 6.128 and 4.901 respectively,and matching the health index and multi-model similarity greatly improves the prediction accuracy of engine remaining life.
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