航空发动机涡轮叶片裂纹检测信号特征提取

doi:10.3969/j.issn.1000-1093.2014.08.020

兵工学报 ›› 2014, Vol. 35 ›› Issue (8): 1267-1274.doi: 10.3969/j.issn.1000-1093.2014.08.020

航空发动机涡轮叶片裂纹检测信号特征提取

于霞¹，张卫民¹，邱忠超¹，陈国龙¹, 赵敦慧²

（1.北京理工大学机械与车辆学院北京 100081;2.中国兵器工业导航与控制技术研究所北京 100089)

收稿日期:2013-09-22 修回日期:2013-09-22 上线日期:2014-11-03
作者简介:于霞(1977—)，女，副教授
基金资助:
总装备部“十二五”预先研究项目（051317030204）

Signal Feature Extraction of Aero-engine Turbine Blade Crack Detection

YU Xia¹, ZHANG Wei-min¹, QIU Zhong-chao¹, CHEN Guo-long¹, ZHAO Dun-hui²

(1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081，China;2.Navigation and Control Technology Research Institute, China North Industries Group Corporation, Beijing 100089，China)

Received:2013-09-22 Revised:2013-09-22 Online:2014-11-03

摘要/Abstract

摘要： 航空发动机涡轮叶片是高精密重要器件，其表面微裂纹检测属于不规则曲面检测的一种,是无损检测领域研究的热点和难点。考虑到涡流检测的特有优势，设计了一种不同于传统方式的简单实用且有效的差激励涡流探头，实现对涡轮叶片预制微裂纹的识别。由于叶片表面为曲率变化的弧面，检测过程难免会发生提离,因此获得的检测信号中包含噪声和多个奇异点等多种干扰因素。为保证缺陷位置重要信息不丢失，采用镜像延拓经验模态分解（EMD）重构与小波奇异性检测相结合的方法对得到的微裂纹信号进行处理，滤除了非裂纹位置的多处畸变点影响，有效准确地实现了叶片微裂纹位置的判定。实验结果表明，该方法可以有效降低检测信号的噪声和干扰，准确提取裂纹信号特征信息，对飞机涡轮叶片类零件微缺陷的早期检测和完整有效性评估具有一定的借鉴意义。

关键词: 航空、航天系统工程, 航空发动机涡轮叶片, 微裂纹, 镜像延拓经验模态分解, 小波奇异性

Abstract: The aero-engine turbine blade is a high precision part. Blade surface crack detection belongs to an irregular surface detection, and is a hotspot and difficulty in the field of non-destructive testing. Taking the unique advantage of eddy current testing into account, a simple，practical and effective differential incentive eddy current probe is designed to detect the prefabricated micro-cracks of aero-engine turbine blades. Since the surface curvature of turbine blade varies, the lift-off effect exists inevitably in the detection process. Therefore, the test results of the detection signal contain noise and multiple singular points. In order to ensure that the important information of defect location is not lost, the combination of the mirror extension empirical mode decomposition（EMD）reconstruction and wavelet singularity detection method is used to process the detected micro-crack signals, filter out the influence of several distortion points on non-cracked positions, and achieve an accurate and effective determination of the micro-crack position of turbine blade. The results show that the method can effectively reduce the noise and interference of detection signal, and extract accurately the feature information of crack signal.

Key words: aerospace system engineering, aero-engine turbine blade, micro-crack, mirror extension EMD, wavelet singularity

中图分类号:

TG115.28

于霞，张卫民，邱忠超，陈国龙, 赵敦慧. 航空发动机涡轮叶片裂纹检测信号特征提取[J]. 兵工学报, 2014, 35(8): 1267-1274.

YU Xia, ZHANG Wei-min, QIU Zhong-chao, CHEN Guo-long, ZHAO Dun-hui. Signal Feature Extraction of Aero-engine Turbine Blade Crack Detection[J]. Acta Armamentarii, 2014, 35(8): 1267-1274.

参考文献

［1］ Wang J，Wang J L，Roberts C． Reducing GPS carrier phase errors with EMD-wavelet for precise static positioning[J]．Survey Review, 2009, 41(312):152-161．
[2] Chen S H, Zhang R H, Su H B, et al. SAR and multispectral image fusion using generalized IHS transform based on à trous wavelet and EMD decompositions[J]． IEEE Sensors Journal, 2010, 10(3):737-745．
[3] 杨露，沈怀荣．希尔伯特一黄变换与小波变换在故障特征提取中的对比研究[J]．兵工学报，2009,30(5)：628-632．
YANG Lu，SHEN Huai-rong．Research and comparison on the application of Hilbert-Huang transform and wavelet transform to fault feature extraction[J]．Acta Armamentarii, 2009 , 30(5):628-632．（in Chinese)
[4] Kopsinis Y，McLaughlin S. Development of EMD-based denoising methods inspired by wavelet thresholding[J]．Signal Processing, 2009, 57(4):1351-1362．
［5］ Gravier B M, Napal N J, Pelstring J A. An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers［C］∥Proceedings of the International Offshore and Polar Engineering Conference. Stavanger, Norway: International Offshore and Polar Engineers, 2001: 268-275.
［6］于德介，程军圣，杨宇.机械故障诊断的Hilbert-Huang变换方法［M］.北京：科学出版社，2006.
YU De-jie, CHENG Jun-sheng, YANG Yu. Hilbert-Huang transform for mechanical fault diagnosis［M］. Beijing: Science Press,
2006. (in Chinese)
［7］胡劲松.面向旋转机械故障诊断的经验模态分解时频分析方法及实验研究［D］.杭州：浙江大学，2003.
HU Jin-song. Experimental research and time-frequency analysis of the rotating machinery faults diagnosis［D］. Hangzhou: Zhejiang University, 2003. (in Chinese)
[8] Omitaomu O A, Protopopescu V A, Ganguly A R. Empirical mode decomposition technique with conditional mutual information for denoising operational sensor data[J]．IEEE Sensors Journal,2011, 11(10): 2565-2575．
[9] Labate D, Foresta F L, Occhiuto G, et al. Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: a comparison[J]．IEEE Sensors Journal, 2013, 13(7):2666-2674．
[10] Zhao L, Huang D R, Cheng F B. Transient characteristic extraction based on wavelet packet decomposition and EMD[C]∥2013 25th Chinese Control and Decision Conference. Guiyang:IEEE,2013:4303-4306．
[11] 赵学智，叶邦彦，陈统坚．基于小波—奇异值分解差分谱的弱故障特征提取方法[J]．机械工程学报，2012，48（7）：37-48．
ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian．Extraction method of faint fault feature based on wavelet-SVD difference spectrum[J]．Chinese Journal of Mechanical Engineering，2012，48(7):37-48．（in Chinese）
[12] 李月琴，栗苹，闰晓鹏，等．小波变换模极大去噪法在无线电引信信号处理中的应用[J]．兵工学报，2008,29(10)：1172-1176．
LI Yue-qin，LI Ping，YAN Xiao-peng，et al. Wavelet transform modulus maximum denoising method for radio fuze signal processing[J]. Acta Armamentarii,2008, 29(10): 1172-1176．（in Chinese）

[1]	朱小婷，张君，王璐，陈志菲，鲍明，王翊. 基于小波奇异特征约束的期望最大时延估计算法[J]. 兵工学报, 2024, 45(4): 1108-1116.
[2]	葛英飞，徐九华，杨辉. SiC_p/Al复合材料超精密车削切屑形成机制及形成过程模型[J]. 兵工学报, 2015, 36(5): 911-920.
[3]	严峰，陈晓，王新民，彭程，胡亚洲. 改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用[J]. 兵工学报, 2014, 35(11): 1914-1921.
[4]	赵建忠，徐廷学，刘勇，尹延涛. 基于粗糙集和熵权以及改进支持向量机的导弹备件消耗预测[J]. 兵工学报, 2012, 33(10): 1258-1265.

航空发动机涡轮叶片裂纹检测信号特征提取

Signal Feature Extraction of Aero-engine Turbine Blade Crack Detection

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价