基于EWT-熵值方法的发动机风扇叶片损伤监控

徐建新; 赵树杰; 马超; 巴翔

doi:10.13224/j.cnki.jasp.20210365

基于EWT-熵值方法的发动机风扇叶片损伤监控

doi: 10.13224/j.cnki.jasp.20210365

徐建新¹,
赵树杰^1,*, ,,
马超¹,
巴翔²

1.
中国民航大学航空工程学院，天津 300300
2.
中国南方航空股份有限公司河南航空有限公司，郑州 450000

基金项目: 波音基金技术挑战项目（20200618073）

详细信息

作者简介:
徐建新（1967-），男，教授，博士，主要从事飞机、发动机的运维技术研究

通讯作者:
赵树杰（1996-），男，硕士，主要从事航空发动机故障诊断研究。E-mail：254202828@qq.com

中图分类号: V231.92
计量
- 文章访问数: 181
- HTML浏览量: 21
- PDF量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2021-07-12
- 网络出版日期: 2022-10-13

Damage monitoring of engine fan blades based on EWT-entropy method

XU Jianxin¹,
ZHAO Shujie^{1,*
, ,},
MA Chao¹,
BA Xiang²

1.
College of Aeronautical Engineering， Civil Aviation University of China，Tianjin 300300，China
2.
Henan Airlines Company Limited， China Southern Airlines Company Limited，Zhengzhou 450000，China

摘要

摘要:
为了从发动机性能数据中寻找风扇叶片外来物损伤航班特征，从而将风扇叶片受外来物损伤的航班区分出来，在机载快速存储记录器（quick access recorder, QAR）数据检测中提出将经验小波分解和信息熵结合的方法。通过对各航班原始振动数据的拟合平滑处理和经验小波分解，提取了分解后各模态的能量熵和，分析了添加汉明（Hanmming）窗函数的多尺度熵，结果表明：拟合后数据的熵值变化更明显，且风扇叶片受外来物损伤航班的能量熵和有10%以上的降低趋势，改进后的多尺度熵有40%以上的增长趋势，均明显异于其他正常航班。证明采用EWT-熵值方法可以较好地对发动机风扇叶片外来物损伤情况进行监控。
- 外来物损伤 /
- 机载QAR数据 /
- 经验小波分解 /
- 能量熵 /
- 多尺度熵
Abstract:
In order to find the characteristics of the flight damaged by foreign objects on the fan blades from the engine performance data, so as to distinguish the flights damaged by the foreign objects on the fan blades, a method combining empirical wavelet transform and information entropy was proposed in the airborne quick access recorder (QAR）data detection. Through the fitted and smoothed process and empirical wavelet transform of the original vibration data of each flight, the sum of the energy entropy of each intrinsic mode function after decomposition was extracted, and the multi-scale entropy of the added window function was analyzed. The result showed the entropy value of the fitted data changed more obviously, and the sum of the energy entropy of the flight with fan blades damaged by foreign objects had a decreasing trend of more than 10%, and the improved multi-scale entropy had an increasing trend of more than 40%, which was obviously different from other normal flights. It is proved that the EWT-entropy method can better monitor the damage by foreign objects of the engine fan blades.
- foreign object damage /
- airborne QAR data /
- empirical wavelet transform /
- energy entropy /
- multiscale entropy

HTML

图 1 C飞机右发正常航班振动图像拟合

Figure 1. Image fitting of vibration of C aircraft flight with normal right engine

下载: 全尺寸图片幻灯片

图 2 A飞机连续28个航班EWT能量熵和

Figure 2. EWT with the sum of energy entropies of 28 consecutive flights of A aircraft

下载: 全尺寸图片幻灯片

图 3 B飞机双发连续26个航班EWT能量熵和

Figure 3. EWT with the sum of energy entropies of 26 consecutive flights of B aircraft twin-engine

下载: 全尺寸图片幻灯片

图 4 C飞机双发连续26个航班EWT能量熵和

Figure 4. EWT with the sum of energy entropies of 26 consecutive flights of C aircraft twin-engine

下载: 全尺寸图片幻灯片

图 5 MSE粗粒化构造方式

Figure 5. MSE coarse-grained structure

下载: 全尺寸图片幻灯片

图 6 A飞机28个航班IMF16 HMSE

Figure 6. IMF16 HMSE of 28 flights of A aircraft

下载: 全尺寸图片幻灯片

图 7 A飞机28个航班IMF1 HMSE

Figure 7. IMF1 HMSE of 28 flights of A aircraft

下载: 全尺寸图片幻灯片

图 8 A飞机28个航班IMF2 HMSE

Figure 8. IMF2 HMSE of 28 flights of A aircraft

下载: 全尺寸图片幻灯片

图 9 A飞机FOD航班保留模态均方误差

Figure 9. Retained modal mean square error of A aircraft FOD flight

下载: 全尺寸图片幻灯片

图 10 B飞机FOD航班特征验证

Figure 10. Verification of flight characteristics of B aircraft FOD

下载: 全尺寸图片幻灯片

图 11 C飞机FOD航班特征验证

Figure 11. Verification of flight characteristics of C aircraft FOD

下载: 全尺寸图片幻灯片

表 1 部分FOD损伤描述

Table 1. Part of FOD damage description

飞机编号	损伤发现日期	故障描述
A	2019年 12月18日	左发4号叶片距叶尖3 mm位置有一豁口，长度约为4.14 mm，深度为不到1 mm，做记录
B	2020年 1月8日	右发3号叶片前缘距叶尖27.4 cm位置有一长度为3 mm、深度为不到1 mm缺口，航后拆下叶片交金工已完成打磨和探伤
C	2019年 8月16日	航后检查发现右发12号叶片叶尖有一处缺口，深度为0.79 mm，长度为4.7 mm，手册标准深度不超过1 mm，按手册检查不超标，留作记录

下载: 导出CSV

表 2 损伤航班能量熵和变化比例统计

Table 2. Statistics of the sum of energy entropies of damage flight and change ratio

飞机	能量熵和均值/nat	损伤航班能量熵和/nat	降低比例/%
A	0.67464	0.581654	13.78
A	0.67464	0.235134	65.15
B	0.612414	0.541216	11.63
		0.553173	9.67
		0.523676	14.49
C	0.235001	0.14828	36.90
		0.177419	24.50
		0.144154	38.66
		0.162222	30.97

下载: 导出CSV

表 3 A飞机部分模态MSE与HMSE标准差

Table 3. Standard deviation of A aircraft partial modal MSE and HMSE

内涵模态	标准差/nat
内涵模态	MSE	HMSE
IMF1	0.02936	0.02034
IMF2	0.044658	0.04717
$\vdots $	$\vdots $	$\vdots $
IMF17	0.1491	0.126287
IMF18	0.128497	0.111078
IMF19	0.138515	0.116193

下载: 导出CSV

表 4 A飞机20个内涵模态28个航班决定系数均值

Table 4. Average value of determination coefficient of 28 flights for 20 intrinsic mode functions of A aircraft

内涵模态	$ {R^2} $均值/nat	内涵模态	$ {R^2} $均值/nat
IMF1	0.8962262	IMF11	0.2035216
IMF2	0.7579664	IMF12	0.3174590
IMF3	0.8224329	IMF13	0.2056715
IMF4	0.8420283	IMF14	0.0511563
IMF5	0.6808552	IMF15	−0.2335838
IMF6	0.5581359	IMF16	−0.13266203
IMF7	0.6555249	IMF17	−0.86031928
IMF8	0.3090201	IMF18	−0.46340345
IMF9	0.3602505	IMF19	−0.59026271
IMF10	0.3678542	IMF20	0.7740957

下载: 导出CSV

表 5 B和C飞机部分内涵模态决定系数均值

Table 5. Average value of determination coefficient for the partial intrinsic mode functions of B and C aircraft

飞机	保留模态	$ {R^2} $均值/nat
B	IMF22	0.6539462
C	IMF8	0.7409416

下载: 导出CSV

表 6 异常航班的样本熵增长比例

Table 6. Sample entropy growth ratio of abnormal flight

飞机	异常保留模态	异常尺度因子	样本熵均值/nat	异常航班样本熵/nat	增长比例/%
A	IMF2	1	0.0271	0.0872	221.74
		2	0.0035	0.0056	58.58
		3	0.0348	0.1065	206.44
		4	0.0518	0.1695	227.38
		5	0.0696	0.2405	245.55
		6	0.0879	0.3079	250.28
		7	0.1070	0.3729	248.50
		8	0.1258	0.4125	227.90
		9	0.1452	0.4463	207.37
		10	0.1648	0.4694	184.83
		11	0.1849	0.4850	162.30
		12	0.2055	0.5149	150.56
		13	0.2261	0.5425	139.94
		14	0.2441	0.5417	121.92
		15	0.2599	0.5355	106.04
		16	0.2762	0.5147	86.35
		17	0.2901	0.5558	91.58
		18	0.3005	0.4907	63.29
		19	0.3085	0.4658	50.98
		20	0.3210	0.5173	61.15
B	IMF22	3	0.5036	0.8405	66.89
		4	0.5021	0.8904	77.33
		5	0.5084	0.8096	59.24
		6	0.5305	0.8015	51.08
		7	0.5667	0.8080	42.57
		8	0.5569	0.8057	44.69
		9	0.5133	0.8118	58.16
		10	0.4368	0.7703	76.35
		11	0.3806	0.6728	76.77
		12	0.3353	0.7218	115.26
		13	0.2858	0.7047	146.57
		14	0.2342	0.6081	159.64
		15	0.1922	0.5027	161.55
		16	0.1472	0.4507	206.18
		17	0.1222	0.4596	276.10
		18	0.0949	0.3546	273.65
		19	0.0655	0.2145	227.48
		20	0.0588	0.1959	233.16
C	IMF8	13	0.2583	0.3757	45.45
		14	0.2347	0.4420	88.32
		15	0.2184	0.3697	69.27
		16	0.2152	0.4321	100.78
		17	0.2069	0.4966	140.01
		18	0.2137	0.5028	135.33
		19	0.2192	0.5288	141.21
		20	0.2323	0.4648	100.06

下载: 导出CSV

参考文献(22)

[1]	马超,王玉娜,武耀罡,等. 航空发动机风扇叶片硬物冲击损伤特征[J]. 航空动力学报,2017,32(4): 1105-1111. MA Chao,WANG Yuna,WU Yaogang,et al. Hard object impact damage characteristics of aero engine fan blade[J]. Journal of Aerospace Power,2017,32(4): 1105-1111. (in Chinese)
[2]	赵烨. 基于卷积神经网络的叶片损伤识别方法研究[D]. 天津: 中国民航大学, 2019. ZHAO Ye. Research on blade damage identification method based on convolution neural network[D]. Tianjin: Civil Aviation University of China, 2019. (in Chinese)
[3]	张维亮. 航空发动机叶片损伤图像快速识别技术研究[D]. 辽宁: 沈阳航空航天大学, 2014. ZHANG Weiliang. Research on technologies of aero-engine blades damage image fast recognition[D]. Liaoning: Shenyang Aerospace University, 2014. (in Chinese)
[4]	程弓. 基于谱聚类算法的航空发动机故障诊断[D]. 上海: 上海交通大学, 2018. CHENG Gong. Aero-engine fault diagnosis based on spectral clustering algorithm[D]. Shanghai: Shanghai Jiao Tong University, 2018. (in Chinese)
[5]	林敏. 极限学习机在航空发动机气路故障诊断中的应用[D]. 上海: 上海交通大学, 2015. LIN Min. Application of extreme learning machine in aero-engine gas path fault diagnosis[D]. Shanghai: Shanghai Jiao Tong University, 2015. (in Chinese)
[6]	张强波,雷晓波. 风扇叶片外物撞击瞬间转子振动响应分析[J]. 科学技术与工程,2020,20(6): 2483-2488. doi: 10.3969/j.issn.1671-1815.2020.06.054 ZHANG Qiangbo,LEI Xiaobo. Analysis of rotor vibration transient response of fan blade impact[J]. Science Technology and Engineering,2020,20(6): 2483-2488. (in Chinese) doi: 10.3969/j.issn.1671-1815.2020.06.054
[7]	舒畅,程铭,许煜,等. 不同金属硬物冲击航空发动机叶片损伤研究[J]. 航空动力学报,2020,35(1): 18-29. doi: 10.13224/j.cnki.jasp.2020.01.003 SHU Chang,CHENG Ming,XU Yu,et al. Research on damage of aero-engine blades impacted by different metal hard objects[J]. Journal of Aerospace Power,2020,35(1): 18-29. (in Chinese) doi: 10.13224/j.cnki.jasp.2020.01.003
[8]	SHARMA V,RAGHUWANSHI N K,JAIN A K. Sensitive sub-band selection criteria for empirical wavelet transform to detect bearing fault based on vibration signals[J]. Journal of Vibration Engineering and Technologies,2021,9(7): 1-15.
[9]	IMAOUCHEN Y,KEDADOUCHE M,ALKAMA R,et al. A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection[J]. Mechanical Systems and Signal Processing,2017,82: 103-116. doi: 10.1016/j.ymssp.2016.05.009
[10]	余志锋,熊邦书,熊天旸,等. 基于VMD-CWT和改进CNN的直升机轴承故障诊断[J]. 航空动力学报,2021,36(5): 948-958. doi: 10.13224/j.cnki.jasp.2021.05.006 YU Zhifeng,XIONG Bangshu,XIONG Tianyang,et al. Fault diagnosis of helicopter bearing based on VMD-CWT and improved CNN[J]. Journal of Aerospace Power,2021,36(5): 948-958. (in Chinese) doi: 10.13224/j.cnki.jasp.2021.05.006
[11]	陈国焉. 浅谈涡扇发动机故障的QAR数据分析[J]. 航空维修与工程,2012(2): 44-46. doi: 10.3969/j.issn.1672-0989.2012.02.011 CHEN Guoyan. QAR data analysis of turbofan engine[J]. Aviation Maintenance and Engineering,2012(2): 44-46. (in Chinese) doi: 10.3969/j.issn.1672-0989.2012.02.011
[12]	徐建新,姜春生,马超. 基于ARIMA模型的民用航空发动机低压转子振动故障分析[J]. 科学技术与工程,2019,19(19): 362-368. XU Jianxin,JIANG Chunsheng,MA Chao. Vibration fault analysis of low pressure rotor of civil aero-engine based on ARIMA model[J]. Science Technology and Engineering,2019,19(19): 362-368. (in Chinese)
[13]	李扬. 平滑样条回归模型变点估计[D]. 贵阳: 贵州大学, 2020. LI Yang. Change point estimation of smoothing spline regression model[D]. Guiyang: Guizhou University, 2020. (in Chinese)
[14]	赵阳. 基于改进经验小波变换的风电机组齿轮箱故障诊断[D]. 北京: 北京交通大学, 2020. ZHAO Yang. Fault diagnosis of wind turbine gearbox based on improved empirical wavelet transform[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
[15]	王莹. 基于成分分解的自适应滤波降噪方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017. WANG Ying. Research on denoising method of adaptive filter based on component decomposition[D]. Harbin: Harbin Institute of Technology, 2017. (in Chinese)
[16]	鲁铁定,黄佳伟,鲁春阳,等. 基于EWT- ARMA的短期电离层TEC预测模型[J]. 大地测量与地球动力学,2021,41(4): 331-335,341. LU Tieding,HUANG Jiawei,LU Chunyang,et al. Short-term ionospheric TEC prediction model based on EWT-ARMA[J]. Journal of Geodesy and Geodynamics,2021,41(4): 331-335,341. (in Chinese)
[17]	周静雷,颜婷. 应用变分模态分解及能量熵的扬声器异常声分类[J]. 声学学报,2021,46(2): 263-270. ZHOU Jinglei,YAN Ting. Loudspeaker abnormal sound classification using variational modal decomposition and energy entropy[J]. Acta Acustica,2021,46(2): 263-270. (in Chinese)
[18]	赵振华,张钧贺,王凌峰,等. 钛合金叶片前缘的外物损伤残余应力数值分析[J]. 航空动力学报,2020,35(11): 2284-2292. ZHAO Zhenhua,ZHANG Junhe,WANG Lingfeng,et al. Numerical analysis on residual stress of foreign object damage on the leading edge of titanium alloy blade[J]. Journal of Aerospace Power,2020,35(11): 2284-2292. (in Chinese)
[19]	张海姣. 基于熵理论的滚动轴承故障诊断方法研究[D]. 合肥: 安徽大学, 2019. ZHANG Haijiao. Research on fault diagnosis method of rolling bearing based on entropy theory[D]. Hefei: Anhui University, 2019. (in Chinese)
[20]	黄寒华. 关于滑动DFT算法中的频谱泄漏问题的探讨[J]. 现代电子技术,2007(24): 202-204. doi: 10.3969/j.issn.1004-373X.2007.24.071 HUANG Hanhua. An exploration of the sliding DFT spectral leakage[J]. Modern Electronics Technique,2007(24): 202-204. (in Chinese) doi: 10.3969/j.issn.1004-373X.2007.24.071
[21]	常峥,罗萍,杨波,等. 基于HHT-MFCC和短时能量的慢性阻塞性肺病患者呼吸声识别[J]. 计算机应用,2021,41(2): 598-603. doi: 10.11772/j.issn.1001-9081.2020060881 CHANG Zheng,LUO Ping,YANG Bo,et al. Respiratory sound recognition of chronic obstructive pulmonary disease patients based on HHT-MFCC and short-term energy[J]. Journal of Computer Applications,2021,41(2): 598-603. (in Chinese) doi: 10.11772/j.issn.1001-9081.2020060881
[22]	陈书炎. 基于改进多尺度样本熵与参数优化支持向量机的滚动轴承故障诊断研究[D]. 镇江: 江苏大学, 2020. CHEN Shuyan. Study on rolling bearing fault diagnosis using improved multiscale sample entropy and parameters optimization SVM[D]. Zhenjiang: Jiangsu University, 2020. (in Chinese)