基于重加权谱峭度方法的航空发动机故障诊断

张忠强; 张新; 王家序; 刘治汶

doi:10.7527/S1000-6893.2021.25445

航空学报 >

2022 , Vol. 43 >Issue 9: 625445 - 625445

DOI: https://doi.org/10.7527/S1000-6893.2021.25445

航空发动机运行安全专栏

基于重加权谱峭度方法的航空发动机故障诊断

张忠强 ,
张新 ,
王家序 ,
刘治汶

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1. 西南交通大学机械工程学院, 成都 610031;
2. 电子科技大学自动化工程学院, 成都 611731

收稿日期: 2021-03-04

修回日期: 2021-03-17

网络出版日期: 2021-05-20

基金资助

国家自然科学基金(52075456, 52075080)；中央高校基本科研业务费专项资金(2682021CX021)

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Reweighted kurtogram for aero-engine fault diagnosis

ZHANG Zhongqiang ,
ZHANG Xin ,
WANG Jiaxu ,
LIU Zhiwen

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1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received date: 2021-03-04

Revised date: 2021-03-17

Online published: 2021-05-20

Supported by

National Natural Science Foundation of China (52075456, 52075080); Fundamental Research Funds for the Central Universities (2682021CX021)

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摘要

针对快速谱峭度方法在分析含强冲击干扰信号时无法选取有效滤波器参数的问题, 定义了一种新的滤波器参数选择指标——重加权峭度, 并基于此提出了重加权谱峭度方法。该方法对经“Binary-Ternary”小波包分解后的各频段信号进行等分并计算各等分频段的峭度及所占其和的权重, 然后对各峭度和权重进行重排序并对峭度进行重加权得到重加权峭度, 最后将新指标表示在中心频率和带宽平面上得到重加权峭度图, 选取滤波器参数。仿真信号分析结果显示, 在强冲击干扰下重加权谱峭度方法仍能选择有效滤波器参数, 提取到周期性故障冲击。通过在航空发动机附齿轮箱中轴承故障诊断中的应用以及与常见方法的对比分析, 进一步验证了重加权谱峭度方法的有效性与优势。

关键词： 航空发动机; 故障诊断; 重加权谱峭度; 重加权峭度; 快速谱峭度; 滤波器参数

本文引用格式

张忠强 , 张新 , 王家序 , 刘治汶 . 基于重加权谱峭度方法的航空发动机故障诊断[J]. 航空学报, 2022 , 43(9) : 625445 -625445 . DOI: 10.7527/S1000-6893.2021.25445

Abstract

To address the problem that the fast Kurtogram method cannot select effective filter parameters when analyzing some real vibration signals with strong impulse interferences, a reweighted kurtogram is proposed, where a new indicator-reweighted kurtosis, is defined. Firstly, the frequency bands obtained by decomposition of the "Binary-Ternary" wavelet packet are equally split into several segments. The kurtosis of each segment and their corresponding weights in their sum are calculated. Then, the kurtosis and weights are sorted in the descending and ascending order, and the reweighted kurtosis is calculated using the reordered kurtosis and weights. Finally, the reweighted kurtogram is obtained by representing the reweighted kurtosis in the plane of central frequency and bandwidth frequency. The simulated signal analysis results show the method proposed is still effective in extracting periodic fault impulses, even when the signal contains strong impulse interferences. Application in fault diagnosis of the aero-engine and comparisons with conventional methods further verify the effectiveness and advantages of the method.

Key words： aero-engine; fault diagnosis; reweighted kurtogram; reweighted kurtosis; fast kurtogram; filter parameter

参考文献

[1] PI J, MA S, HE J C, et al. Rolling bearing fault diagnosis based on IGA-ELM network[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(9): 422036 (in Chinese). 皮骏, 马圣, 贺嘉诚, 等. 基于IGA-ELM网络的滚动轴承故障诊断[J]. 航空学报, 2018, 39(9): 422036.
[2] SUN C F, WANG Y R. Advance in study of fault di-agnosis of helicopter planetary gears[J]. Acta Aero-nautica et Astronautica Sinica, 2017, 38(7): 020892 (in Chinese). 孙灿飞, 王友仁. 直升机行星传动轮系故障诊断研究进展[J]. 航空学报, 2017, 38(7): 020892.
[3] WANG S P. Prognostics and health management key technology of aircraft airborne system[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(6): 1459-1472 (in Chinese). 王少萍. 大型飞机机载系统预测与健康管理关键技术[J]. 航空学报, 2014, 35(6): 1459-1472.
[4] ZHANG X, MIAO Q, ZHANG H, et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 108: 58-72.
[5] CHEN X F, GUO Y J, XU C B, et al. Review of fault diagnosis and health monitoring for wind power equipment[J]. China Mechanical Engineering, 2020, 31(2): 175-189 (in Chinese). 陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2): 175-189.
[6] HE Z Y, CHEN G, HE C, et al. MED optimal filter length selection: New method and applications[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 423658 (in Chinese). 贺志远, 陈果, 何超, 等. 一种MED最优滤波长度选择新方法及其应用[J]. 航空学报, 2020, 41(10): 423658.
[7] MENG T, LIAO M F. Detection and diagnosis of the rolling element bearing fault by the delayed correlation-envelope technique[J]. Acta Aeronautica et Astronautica Sinica, 2004, 25(1): 41-44 (in Chinese). 孟涛, 廖明夫. 利用时延相关解调法诊断滚动轴承的故障[J]. 航空学报, 2004, 25(1): 41-44.
[8] XIONG Q C, ZHANG X, WANG J X, et al. Sparse representations for fault signatures via hybrid regularization in adaptive undecimated fractional spline wavelet transform domain[J]. Measurement Science and Technology, 2021, 32(4): 045107.
[9] ZHANG X, LIU Z W, WANG L, et al. Bearing fault diagnosis based on sparse representations using an improved OMP with adaptive Gabor sub-dictionaries[J]. ISA Transactions, 2020, 106: 355-366.
[10] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124.
[11] BARSZCZ T, RANDALL R B. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1352-1365.
[12] LEI Y G, LIN J, HE Z J, et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1738-1749.
[13] WANG Y, TSE P W, TANG B P, et al. Kurtogram manifold learning and its application to rolling bearing weak signal detection[J]. Measurement, 2018, 127: 533-545.
[14] LI F L. Compound-fault diagnosis of rolling bearings in a high-speed train based on improved fast kurtogram[D]. Chengdu: Southwest Jiaotong University, 2019: 21-24 (in Chinese). 李凤林. 基于改进快速峭度图的高速列车滚动轴承复合故障诊断[D]. 成都: 西南交通大学, 2019: 21-24.
[15] TAN J Y, CHEN X F, HE Z J. Impact signal detection method with adaptive stochastic resonance[J]. Journal of Mechanical Engineering, 2010, 46(23): 61-67 (in Chinese). 谭继勇, 陈雪峰, 何正嘉. 冲击信号的随机共振自适应检测方法[J]. 机械工程学报, 2010, 46(23): 61-67.
[16] DAI S C, GUO Y, WU X, et al. Improvement on fast kurtogram algorithm based on sub-frequency-band spectral kurtosis average[J]. Journal of Vibration and Shock, 2015, 34(7): 98-102, 108 (in Chinese). 代士超, 郭瑜, 伍星, 等. 基于子频带谱峭度平均的快速谱峭度图算法改进[J]. 振动与冲击, 2015, 34(7): 98-102, 108.
[17] WANG L, LIU Z W, CAO H R, et al. Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2020, 142: 106755.
[18] ANTONI J. The spectral kurtosis: a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282-307.
[19] DWYER R. Detection of non-Gaussian signals by frequency domain Kurtosis estimation[C]//ICASSP'83. IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE Press, 1983: 607-610.
[20] ANTONI J. Fast Kurtogram[EB/OL]. (2015-05-18)[2020-11-24]. https://www.mathworks.com/matlabcentral/fileexchange/48912-fast-kurtogram.
[21] ANTONI J, GRIFFATON J, ANDRé H, et al. Feedback on the Surveillance 8 challenge: vibration-based diagnosis of a Safran aircraft engine[J]. Mechanical Systems and Signal Processing, 2017, 97: 112-144.

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