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

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

沈阳航空航天大学辽宁省航空推进系统先进测试技术重点实验室，沈阳 110136

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
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- 文章访问数: 140
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-21
- 网络出版日期: 2023-10-27

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

Key Laboratory of Advanced Measurement and Test Technology for Aviation Propulsion System，Shenyang Aerospace University，Shenyang 110136，China

摘要

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%。
- 中介轴承 /
- 振动信号 /
- 广义精细复合多尺度量子熵 /
- 核主成分分析 /
- 故障诊断
Abstract:
In view of the problems of complex paths of inter-shaft bearing vibration signal transmission to the measurement surface of the magazine, which lead to difficulties in fault feature extraction and identification, a fault diagnosis method based on generalized refined composite multiscale quantum entropy (GRCMQE), kernel principal component analysis (KPCA) and parameter optimization support vector machine was proposed for inter-shaft bearing fault diagnosis. Firstly, GRCMQE was used to extract fault features from vibration signals, and high-dimensional fault feature sets were constructed. Secondly, KPCA method was utilized to reduce the dimension of high-dimensional feature data to obtain low dimensional manifold features. Then, the obtained features were input into the support vector machine based on cross validation to complete the fault pattern recognition. Finally, the proposed method was tested on the intermediate bearing fault data set, and the results showed that the method can effectively identify different fault types of intermediate bearing, with the fault identification accuracy up to 98.33%.
- inter-shaft bearing /
- vibration signal /
- generalized refined composite multiscale quantum entropy /
- kernel principal component analysis /
- fault diagnosis

HTML

图 1 广义精细复合多尺度量子熵计算流程图

Figure 1. Flowchart of GRCMQE

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图 2 噪声信号波形图

Figure 2. Waveforms of noise signals

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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

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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

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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

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图 6 KPCA降维算法示意图

Figure 6. Schematic diagram of KPCA dimensionality reduction algorithm

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图 7 中介轴承故障诊断流程图

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

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图 8 中介轴承故障模拟实验台

Figure 8. Inter-shaft bearing fault simulation test bench

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图 9 加速度传感器示意图

Figure 9. Schematic diagram of acceleration sensors

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图 10 实验轴承

Figure 10. Bearings used in the test

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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

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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

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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

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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

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图 15 KPCA特征融合结果

Figure 15. KPCA feature fusion results

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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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