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基于广义精细复合多尺度量子熵和核主成分分析的中介轴承故障诊断方法

田晶 张羽薇 张凤玲 艾辛平 高崇

田晶, 张羽薇, 张凤玲, 等. 基于广义精细复合多尺度量子熵和核主成分分析的中介轴承故障诊断方法[J]. 航空动力学报, 2024, 39(2):20210467 doi: 10.13224/j.cnki.jasp.20210467
引用本文: 田晶, 张羽薇, 张凤玲, 等. 基于广义精细复合多尺度量子熵和核主成分分析的中介轴承故障诊断方法[J]. 航空动力学报, 2024, 39(2):20210467 doi: 10.13224/j.cnki.jasp.20210467
TIAN Jing, ZHANG Yuwei, ZHANG Fengling, et al. Inter-shaft bearing fault diagnosis method based on generalized refined composite multiscale quantum entropy and kernel principal component analysis[J]. Journal of Aerospace Power, 2024, 39(2):20210467 doi: 10.13224/j.cnki.jasp.20210467
Citation: TIAN Jing, ZHANG Yuwei, ZHANG Fengling, et al. Inter-shaft bearing fault diagnosis method based on generalized refined composite multiscale quantum entropy and kernel principal component analysis[J]. Journal of Aerospace Power, 2024, 39(2):20210467 doi: 10.13224/j.cnki.jasp.20210467

基于广义精细复合多尺度量子熵和核主成分分析的中介轴承故障诊断方法

doi: 10.13224/j.cnki.jasp.20210467
基金项目: 国家自然科学基金(12172231); 沈阳市中青年科技创新人才支持计划(RC220439)
详细信息
    作者简介:

    田晶(1987-),男,教授、硕士生导师,博士,主要从事航空发动机状态监测与故障诊断方面的研究

  • 中图分类号: V231.92

Inter-shaft bearing fault diagnosis method based on generalized refined composite multiscale quantum entropy and kernel principal component analysis

  • 摘要:

    针对中介轴承振动信号传递到机匣测表面上路径复杂,导致故障特征提取及识别困难等问题,提出了一种基于广义精细复合多尺度量子熵(generalized refined composite multiscale quantum entropy, GRCMQE)、核主成分分析(kernel principal component analysis, KPCA)与参数优化支持向量机的中介轴承故障诊断方法。该方法首先采用GRCMQE从振动信号中提取故障特征,构建高维故障特征集。其次,采用KPCA方法对高维特征数据降维,得到低维流形特征。然后,将得到的特征输入到基于交叉验证优化的支持向量机(cross validation- support vector machine, CV-SVM)中,完成故障模式识别。最后,在中介轴承故障数据集上对所提出的方法进行测试,结果表明该方法能够有效实现中介轴承不同故障类型的识别,并且故障识别精度达到98.33%。

     

  • 图 1  广义精细复合多尺度量子熵计算流程图

    Figure 1.  Flowchart of GRCMQE

    图 2  噪声信号波形图

    Figure 2.  Waveforms of noise signals

    图 3  白噪声和粉红噪声在不同数据长度下的MQE、GMQE、GRCMQE

    Figure 3.  MQE、GMQE、GRCMQE of white noise and pink noise under different data lengths

    图 4  广义精细复合多尺度量子熵在不同嵌入维数下对比

    Figure 4.  Comparison analysis of GRCMQE with different embedding dimension

    图 5  广义精细复合多尺度量子熵在不同时延常数下对比

    Figure 5.  Comparison analysis of GRCMQE with different delay constant

    图 6  KPCA降维算法示意图

    Figure 6.  Schematic diagram of KPCA dimensionality reduction algorithm

    图 7  中介轴承故障诊断流程图

    Figure 7.  Flow chart of inter-shaft bearing fault diagnosis

    图 8  中介轴承故障模拟实验台

    Figure 8.  Inter-shaft bearing fault simulation test bench

    图 9  加速度传感器示意图

    Figure 9.  Schematic diagram of acceleration sensors

    图 10  实验轴承

    Figure 10.  Bearings used in the test

    图 11  中介轴承振动信号时域波形图

    Figure 11.  Time domain waveforms of inter-shaft bearing vibration signal

    图 12  不同故障类型Hilbort包络信号频谱图

    Figure 12.  Spectrum diagram of Hilbort envelope signals of different fault types

    图 13  不同故障类型振动信号的GRCMQE特征值

    Figure 13.  GRCMQE of vibration signals of different fault types

    图 14  不同故障类型振动信号的MQE特征值

    Figure 14.  MQE of vibration signals of different fault types

    图 15  KPCA特征融合结果

    Figure 15.  KPCA feature fusion results

    图 16  基于KPCA的CV-SVM分类结果

    Figure 16.  CV-SVM classification results based on KPCA

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  • 收稿日期:  2021-08-21
  • 网络出版日期:  2023-10-27

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