基于最大相关雷尼熵与相空间重构的航空发动机复合故障信号特征提取方法

张震; 刘保国; 周万春; 冯伟

doi:10.13224/j.cnki.jasp.20220609

基于最大相关雷尼熵与相空间重构的航空发动机复合故障信号特征提取方法

doi: 10.13224/j.cnki.jasp.20220609

张震^1,,
刘保国^1,*, ,,
周万春²,
冯伟¹

1.
河南工业大学河南省超硬磨料磨削装备重点实验室，郑州 450001
2.
郑州工程技术学院机电与车辆工程学院，郑州 450044

基金项目: 国家自然科学基金（12072106）；河南省超硬磨料磨削装备重点实验室开放课题（JDKFJJ2022008）；郑州工程技术学技术研发推广与转化基金（zjz202209）

详细信息

作者简介:
张震（1986-），男，讲师，博士生，主要从事信号处理，故障诊断方面的研究。E-mall：zhangzhen1986217@163.com

通讯作者:
刘保国（1962-），男，教授、博士生导师，博士，主要从事转子动力学、机械振动方面的研究。E-mall：bgliu1978@sina.com

中图分类号: V263.6；TH133.33
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-22
- 网络出版日期: 2023-03-08

Composite fault signal feature extraction method for aero-engine based on maximum correlation Rényi entropy and phase space reconstruction

ZHANG Zhen^1
,,
LIU Baoguo^{1,*
, ,},
ZHOU Wanchun²,
FENG Wei¹

1.
Henan Key Laboratory for Superabrasive Grinding Equipment， Henan University of Technology，Zhengzhou 450001，China
2.
School of Mechanical，Electrical and Vehicle Engineering， Zhengzhou University of Technology，Zhengzhou 450044，China

摘要

摘要:
针对低信噪比（SNR），复杂噪声工况下，复合故障信号特征难以提取的问题。提出基于相空间重构融入最大相关雷尼熵解卷积的信号特征提取方法，该方法以雷尼熵为敏感特征范数，以最大相关雷尼熵解卷积为基本方法，并在其中融入具有噪声抑制特性和分解特性的相空间重构技术。结果表明：雷尼熵与峭度相比，在故障灵敏度相当并略好的情况下，对偶发噪声敏感度仅为峭度的18.4%。通过仿真验证，实验数据验证以及台架实验验证，证明了本文方法与现有的对比方法相比，在提取复合故障信号特征方面具有优势。
- 雷尼熵 /
- 相空间重构 /
- 复合故障 /
- 滚动轴承 /
- 解卷积
Abstract:
In order to solve the problem of complex fault signal feature extraction under the condition of low signal-to-noise ratio (SNR) and complex noise, a feature extraction method based on phase space reconstruction and maximum correlation Rényi entropy deconvolution was proposed. Rényi entropy was taken as the performance index, and the maximum correlation Rényi entropy deconvolution was taken as the basic method, and the phase space reconstruction technique was incorporated with the characteristics of noise suppression and decomposition. Results showed that the sensitivity of Raney entropy was only 18.4% of the kurtosis when the fault sensitivity was equal to and slightly better than that of kurtosis. Through simulation, experimental data and bench test, this method was proved superior to existing comparison methods in extracting the features of composite fault signals.
- Rényi entropy /
- phase space reconstruction /
- composite fault /
- rolling bearing /
- deconvolution

HTML

图 1 $ {S}_{\mathrm{k}} $、$ {S}_{\mathrm{r}}、{R}_{\mathrm{e}} $随缺陷变化示意图

Figure 1. $ {S}_{\mathrm{k}} $、$ {S}_{\mathrm{r}}、{R}_{\mathrm{e}} $ schematic diagram of the variation with defects

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图 2 $ {S}_{\mathrm{k}} $、$ {S}_{\mathrm{r}}、{R}_{\mathrm{e}} $对偶发噪声的敏感性

Figure 2. $ {S}_{\mathrm{k}} $、$ {S}_{\mathrm{r}}、{R}_{\mathrm{e}} $ sensitivity to accidental noise

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图 3 典型信号

Figure 3. Typical signal

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图 4 归一化敏感度

Figure 4. Normalized sensitivity

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图 5 方法流程图

Figure 5. Method flow diagram

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图 6 各分量信号时域波形

Figure 6. Time domain waveform of each component signal

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图 7 快速谱峭度分析结果

Figure 7. Results of rapid spectral kurtosis analysis

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图 8 MCKD包络谱

Figure 8. MCKD envelope spectrum

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图 9 FSR-MCRED分析结果

Figure 9. Analysis results of FSR-MCRED

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图 10 发动机结构示意图与齿轮箱结构图

Figure 10. Schematic diagram of engine and gearbox structures

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图 11 快速谱峭度分析结果

Figure 11. Results of rapid spectral kurtosis analysis

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图 12 FSC分析结果

Figure 12. FSC analysis results

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图 13 FSR-MCRED包络谱

Figure 13. FSR-MCRED envelope spectra

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图 14 3种方法的包络谱结果

Figure 14. Three methods of envelope spectra results

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图 15 实验布局图

1 电动机；2 联轴器；3 加速度传感器Ⅰ；4 轴承座Ⅰ；5 主轴；6 转子；7 加速度传感器Ⅱ；8 轴承座Ⅱ；9 轴承Ⅰ；10轴承Ⅱ。

Figure 15. Diagram of experimental layout

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图 16 4种方法包络分析结果

Figure 16. Results of four methods of envelope analysis

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表 1 仿真信号参数

Table 1. Simulation signal parameters

参数	数值
$ {f}_{0}/\mathrm{H}\mathrm{z} $	50
$ {a}_{j} $/g	0.7
M	1
g	0.7
$ {f}_{\mathrm{e}}/\mathrm{H}\mathrm{z} $	10
$ {\tau }_{j} $/s	2%T

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表 2 仿真信号参数

Table 2. Simulation signal parameters

参数	数值及说明
$ \mathrm{采}\mathrm{样}\mathrm{频}\mathrm{率}{f}_{\mathrm{s}} $/Hz	12000
内圈特征频率$ {f}_{\mathrm{i}} $/Hz	213
外圈特征频率$ {f}_{\mathrm{o}} $/Hz	143
转频$ {f}_{\mathrm{r}} $/Hz	2000（转速为12000 r/min）
比率系数$ \mu $	0.3
冲击干扰振幅${D}_{\mathrm{i} }/{g}$	随机变量
随机冲击时间$ {T}_{3}/\mathrm{s} $	随机变量
离散谐波振幅${p}_{{j} }/{g}$	0.03（${p}_{1}/{g}$）， 0.04（${p}_{2}/{g}$），
离散谐波频率${f}_{4{j} }/\mathrm{H}\mathrm{z}$	$45.2 （{f}_{41}），56.7 （{f}_{42}）$
初始相位${\varphi }_{1}$、$ {\varphi }_{2} $、$ {\varphi }_{3} $、$ {\varphi }_{4} $	随机数$\left[-\text{π}，\text{π}\right]$

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表 3 发动机轴承特征频率

Table 3. Characteristic frequency of engine bearings

参数	数值
转频$ {f}_{\mathrm{r}} $/Hz	182.9
滚动体特征频率$ {f}_{\mathrm{b}} $/Hz	650.5
外圈特征频率${f}_{\mathrm{o} }/{\rm{Hz }}$	1419.5
内圈特征频率$ {f}_{\mathrm{i}} $/Hz	1843.5
保持架$ {\mathrm{特}\mathrm{征}\mathrm{频}\mathrm{率}f}_{\mathrm{t}} $/Hz	78.8

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表 4 MBER-12K轴承特征频率

Table 4. Characteristic frequency of MBER-12K bearings

参数	数值
转频$ {f}_{\mathrm{r}} $/Hz	45
滚动体特征频率$ {f}_{\mathrm{b}} $/Hz	89.6
外圈特征频率${f}_{\mathrm{o} }/{\rm{Hz}}$	137
内圈特征频率$ {f}_{\mathrm{i}} $/Hz	222.7
保持架$ {\mathrm{特}\mathrm{征}\mathrm{频}\mathrm{率}f}_{\mathrm{t}} $/Hz	17.01

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