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采用K-SVD字典训练稀疏基的压缩感知叶尖间隙数据重构方法

吴军 冯成斌 宋丰成 袁少博 于之靖

吴军, 冯成斌, 宋丰成, 袁少博, 于之靖. 采用K-SVD字典训练稀疏基的压缩感知叶尖间隙数据重构方法[J]. 机械科学与技术, 2023, 42(7): 1158-1164. doi: 10.13433/j.cnki.1003-8728.20220068
引用本文: 吴军, 冯成斌, 宋丰成, 袁少博, 于之靖. 采用K-SVD字典训练稀疏基的压缩感知叶尖间隙数据重构方法[J]. 机械科学与技术, 2023, 42(7): 1158-1164. doi: 10.13433/j.cnki.1003-8728.20220068
WU Jun, FENG Chengbin, SONG Fengcheng, YUAN Shaobo, YU Zhijing. A Sparse Basis Compressed Sensing Tip Clearance Data Reconstruction Method was Trained with K-SVD Dictionary[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1158-1164. doi: 10.13433/j.cnki.1003-8728.20220068
Citation: WU Jun, FENG Chengbin, SONG Fengcheng, YUAN Shaobo, YU Zhijing. A Sparse Basis Compressed Sensing Tip Clearance Data Reconstruction Method was Trained with K-SVD Dictionary[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1158-1164. doi: 10.13433/j.cnki.1003-8728.20220068

采用K-SVD字典训练稀疏基的压缩感知叶尖间隙数据重构方法

doi: 10.13433/j.cnki.1003-8728.20220068
基金项目: 

国家自然科学基金项目 52005500

中央高校基本科研业务费高水平成果培育专项 3122023PY06

详细信息
    作者简介:

    吴军(1986-), 副教授, 博士研究生, 研究方向为视觉测量技术, 发动机故障诊断与监测, j_wu@cauc.edu.cn

  • 中图分类号: TH822

A Sparse Basis Compressed Sensing Tip Clearance Data Reconstruction Method was Trained with K-SVD Dictionary

  • 摘要: 航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value decomposition)字典训练稀疏基的数据重构方法,该方法首先构建出K-SVD字典稀疏基对数据进行稀疏化表示,然后使用m序列高斯随机矩阵对数据进行压缩观测,最后基于压缩欠采样观测值使用正交匹配追踪算法对数据进行重构,进而精确提取叶尖间隙值。实验结果表明,在欠采样条件下间隙距离数据可精确恢复重构,与高采样率下的间隙数据相比,重构误差不超过0.02 mm。
  • 图  1  基于K-SVD字典训练稀疏基的压缩感知叶尖间隙数据重构方法流程图

    Figure  1.  Flowchart of the compressed sensing reconstruction method for evaluating blade tip clearance data based on K-SVD dictionary training with sparse bases

    图  2  K-SVD字典稀疏基训练流程图

    Figure  2.  Flowchart of the K-SVD dictionary training process for sparse bases

    图  3  叶尖间隙数据重构总体流程图

    Figure  3.  Overall flowchart of blade tip clearance data reconstruction

    图  4  使用K-SVD字典稀疏基的压缩感知OMP算法的叶尖间隙数据重构结果

    Figure  4.  Reconstruction results for blade tip clearance data using the compressed sensing OMP algorithm with K-SVD dictionary sparse bases

    图  5  叶尖间隙数据测量实验平台

    Figure  5.  Experimental platform for blade tip clearance data measurement

    图  6  叶尖间隙实验测量示意图

    Figure  6.  Schematic diagram of the blade tip clearance measurement experiment

    表  1  不同M值时重构平均误差表

    Table  1.   The average error table is reconstructed for different M values

    欠采样观测数M 30 40 50 60 70 80
    重构误差ε/mm 0.29 0.23 0.15 0.01 0.01 e-12
    下载: 导出CSV

    表  2  各方法提取叶尖间隙数据对比表

    Table  2.   Comparison of blade tip clearance data extraction methods

    滑移台位置 叶尖间隙值 欠采样间隙值 欠采样间隙值误差 重构间隙值 重构间隙值误差
    1 0.408 5 0.291 8 0.116 7 0.388 7 0.019 8
    2 0.909 6 0.803 6 0.1060 0.890 0 0.019 6
    3 1.412 3 1.301 9 0.110 4 1.392 0 0.020 3
    4 1.899 6 1.803 7 0.095 9 1.879 7 0.019 9
    5 2.396 5 2.299 6 0.096 9 2.376 5 0.020 0
    6 2.897 2 2.837 3 0.059 9 2.877 1 0.020 1
    下载: 导出CSV
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  • 收稿日期:  2021-07-30
  • 刊出日期:  2023-07-25

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