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多源数据融合的民航发动机修后性能预测

谭治学 钟诗胜 林琳

谭治学, 钟诗胜, 林琳等 . 多源数据融合的民航发动机修后性能预测[J]. 北京航空航天大学学报, 2019, 45(6): 1106-1113. doi: 10.13700/j.bh.1001-5965.2018.0557
引用本文: 谭治学, 钟诗胜, 林琳等 . 多源数据融合的民航发动机修后性能预测[J]. 北京航空航天大学学报, 2019, 45(6): 1106-1113. doi: 10.13700/j.bh.1001-5965.2018.0557
TAN Zhixue, ZHONG Shisheng, LIN Linet al. Commercial aircraft engine post-repairing performance prediction based on fusion of multisource data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(6): 1106-1113. doi: 10.13700/j.bh.1001-5965.2018.0557(in Chinese)
Citation: TAN Zhixue, ZHONG Shisheng, LIN Linet al. Commercial aircraft engine post-repairing performance prediction based on fusion of multisource data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(6): 1106-1113. doi: 10.13700/j.bh.1001-5965.2018.0557(in Chinese)

多源数据融合的民航发动机修后性能预测

doi: 10.13700/j.bh.1001-5965.2018.0557
基金项目: 

国家自然科学基金 U1533202

民航科技项目重大专项 MHRD20150104

山东省自主创新及成果转化专项 2014CGZH1101

详细信息
    作者简介:

    谭治学  男, 博士研究生。主要研究方向:航空发动机健康管理

    钟诗胜   男, 博士, 教授, 博士生导师。主要研究方向:复杂装备健康管理

    林琳   女, 博士, 教授, 博士生导师。主要研究方向:智能设计、装备健康管理

    通讯作者:

    钟诗胜, E-mail: zhongss@hit.edu.cn

  • 中图分类号: TP391;V267;V235.13+3

Commercial aircraft engine post-repairing performance prediction based on fusion of multisource data

Funds: 

National Natural Science Foundation of China U1533202

the Major Project of Civil Aviation Administration of China MHRD20150104

Shandong Independent Innovation and Achievements Transformation Fund 2014CGZH1101

More Information
  • 摘要:

    针对民航发动机修后排气温度裕度预测过程中的多源异构数据融合问题,提出了卷积自编码器与极端梯度提升模型结合的方法。利用所提出的条件熵增长因子规整发动机修前多元传感器参数序列中的参数排序,采用卷积自编码器提取规整后的参数序列和维修工作范围的数据特征,并将其与发动机使用时间信息组成合成特征以训练极端梯度提升模型,从而预测发动机修后性能并评估各影响因素的重要程度。经发动机机队维修案例验证,所提方法预测精度高于单维参数序列预测方法,对发动机修后排气温度的平均相对预测误差不高于8.3%。

     

  • 图 1  发动机修后性能预测整体流程

    Figure 1.  Overall flowchart of engine post-repairing performance prediction

    图 2  单元体维修工作范围数据结构示意图

    Figure 2.  Schematic of data structure of component maintenance workscope

    图 3  航空发动机维修工作范围部分实验数据

    图 4  SAE-1D-XGBoost对TEGTM序列的重构精度

    Figure 4.  Reconstruction precision of TEGTM series by SAE-1D-XGBoost

    图 5  CAE-1D-XGBoost对TEGTM序列的重构精度

    Figure 5.  Reconstruction precision of TEGTM series by CAE-1D-XGBoost

    图 6  各单元体维修工作范围原始值矢量及CAE-1D-XGBoost重构矢量

    Figure 6.  Original component maintenance workscope vector and reconstructed vector by CAE-1D-XGBoost

    图 7  修后TEGTM预测误差与条件熵增长因子的相关性

    Figure 7.  Correlation of prediction error of post-repairing TEGTM and condition entropy increasing factor

    图 8  SAE-1D-XGBoost、CAE-1D-XGBoost、CAE-XGBoost对发动机的修后TEGTM预测误差

    Figure 8.  Prediction error of engine post-repairing TEGTM by SAE-1D-XGBoost, CAE-1D-XGBoost and CAE-XGBoost

    图 9  不同方法综合后各影响因素对发动机修后TEGTM重要性占比

    Figure 9.  Percentage of influential element importance to engine post-repairing TEGTM synthesized by different methods

    表  1  SAE-1D-XGBoost、CAE-1D-XGBoost、CAE-XGBoost对参数序列的重构误差

    Table  1.   Reconstruction error of parameter series by SAE-1D-XGBoost, CAE-1D-XGBoost and CAE-XGBoost

    方法 特征值个数 MSE均值/10-3 ARE均值/10-2 MSE方差/10-6 ARE方差/10-6
    SAE-1D-XGBoost 5 5.288 5.56 12.59 277.9
    SAE-1D-XGBoost 10 4.757 5.29 13.73 347.0
    SAE-1D-XGBoost 15 5.333 5.84 10.28 284.5
    SAE-1D-XGBoost 20 4.573 5.28 10.15 285.0
    CAE-1D-XGBoost 10 4.554 5.44 3.075 136.6
    CAE-1D-XGBoost 20 9.173 8.20 12.30 327.4
    CAE-XGBoost 25 12.60 7.39 0.1259 5.194
    下载: 导出CSV

    表  2  SAE-1D-XGBoost、CAE-1D-XGBoost、CAE-XGBoost对发动机的修后TEGTM预测误差

    Table  2.   Prediction error of engine post-repairing TEGTM by SAE-1D-XGBoost, CAE-1D-XGBoost and CAE-XGBoost

    方法 特征值个数 MSE均值/10-2 ARE均值
    SAE-1D-XGBoost 5 1.919 0.1145
    SAE-1D-XGBoost 10 1.893 0.1083
    SAE-1D-XGBoost 15 2.091 0.1198
    SAE-1D-XGBoost 20 1.903 0.1135
    CAE-1D-XGBoost 10 1.268 0.0860
    CAE-1D-XGBoost 20 1.547 0.1053
    CAE-XGBoost 25(均值) 1.183 0.0889
    CAE-XGBoost 25(规整后) 0.8367 0.0822
    下载: 导出CSV

    表  3  不同方法中影响因素对发动机修后TEGTM的重要性占比

    Table  3.   Percentage of influential element importance to engine post-repairing TEGTM in different methods

    方法 使用时间占比/% 送修性能占比/% 维修工作范围占比/%
    SAE-1D-XGBoost 47.54 12.04 40.42
    CAE-1D-XGBoost 34.33 46.43 19.24
    CAE-XGBoost 39.35 32.11 28.54
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-09-19
  • 录用日期:  2019-01-04
  • 网络出版日期:  2019-06-20

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