基于气动信号分析的风机叶片裂纹故障识别

PDF(713 KB)

振动与冲击 ›› 2017, Vol. 36 ›› Issue (19) : 227-231.

论文

基于气动信号分析的风机叶片裂纹故障识别

黎少辉1 蔡利梅2

作者信息 +

Fan Blade Crack Fault Diagnosis Based on Pneumatic Signals Analysis

Li shao-hui1 Cai li-mei2

Author information +

文章历史 +

摘要

针对风机叶片裂纹故障，提出通过分析风机出口气动信号，实现在线动态检测裂纹的方法。采集叶片不同状态下的风机出口气动信号，利用db4小波对气动信号进行5层分解，并单支重构，将各频带归一化能量构成6维特征向量；对能量向量进行主成分分析，基于贡献率实现特征选择；采用K均值聚类方法进行叶片状态识别。实验结果表明，气动信号能有效反映风机叶片状态的变化，该方法可以实现叶片正常、异常状态检测及裂纹长度状态区分，提供了风机叶片裂纹在线实时检测依据和手段。

Abstract

Based on the analysis of pneumatic signals obtained in fan outlet，the method of dynamic detection of cracks in fan blade was proposed. The original pneumatic signals were collected under different conditions of the fan; then, they were decomposed and reconstructed by multi-resolution wavelet transform. The normalized energy in every frequency band composed six dimension characteristic vector. Principal components analysis (PCA) was used for dimension reduction and feature selection. At last, K-means clustering method was adopted to recognize the condition of fan blade. The results show that pneumatic signal can reflect the state change of fan blade; the method can make a distinction between the normal and abnormal state of fan blade efficiently. It provides the foundation and method for on-line inspection of fan blade crack．

导出引用

黎少辉1 蔡利梅2. 基于气动信号分析的风机叶片裂纹故障识别[J]. 振动与冲击, 2017, 36(19): 227-231

Li shao-hui1 Cai li-mei2 . Fan Blade Crack Fault Diagnosis Based on Pneumatic Signals Analysis[J]. Journal of Vibration and Shock, 2017, 36(19): 227-231

参考文献

[1]王艳丰,朱靖,滕光蓉等.航空发动机转子早期裂纹故障振动特征的维谱分析[J].振动与冲击,2015,34 (1):88-93.
WANG Yan-feng,ZHU Jing,TENG Guang-rong,et al.1.5-dimension spectrum analysis on early cracked fault characters of aero engine rotors[J].Journal of Vibration and Shock,2015,34 (1):88-93.
[2] 于霞,张卫民,邱忠超等.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 Xia1,ZHANG Wei-min1,QIU Zhong-chao1, et al. Signal Feature Extraction of Aero-engine Turbine Blade Crack Detection[J].ACTA AＲMAMENTAＲII, 2014,35(8):1267-1274．
[3] 董利明,李加,倪辰荫等. 基于光热调制检测发动机叶片疲劳裂纹的激光声表面波方法[J], 中国激光,2011,38(11):1103001. Dong Liming, Li Jia, Ni Chenyin, et al. Crack Detection of Engine Blade Based on Laser-Heating Assisted Surface Acoustic Waves Generated by Scanning Laser[J]. CHINESES JOURNAL OF LASERS , 2011,38(11):1103001.
[4] 赵新光,甘晓晔,谷泉等.基于小波能谱系数的风力机疲劳裂纹特征[J],振动、测试与诊断, 2014,34(2):147-154.
Zhao Xinguang, Gan Xiaoye, Gu Quan, et al. Crack Fault Feature of Wind Turbine Blade Based on Wavelet Energy Spectrum Coefficient[J]. Journal of Vibration, Measurement & Diagnosis, 2014,34(2):147-154.
[5] 李录平,李芒芒,晋风华等. 振动检测技术在风力机叶片裂纹故障监测中的应用[J], 热能动力工程,2013,28(3):207-213.
LI Lu-ping,LI Mang-mang,JIN Feng-hua，et al. Applications of the Vibration Detection Technologiesin Monitoring the Blade Crack Fault of Wind Turbines[J]. JOURNAL OF ENGINEERING FOU THERMAL ENERGY AND POWER, 2013,28(3):207-213.
[6] 周勃,谷艳玲,项宏伟等.风力机叶片裂纹扩展预测与疲劳损伤评价[J],太阳能学报, 2015,36(1):41-48.
Zhou Bo1, Gu Yanling, Xiang Hongwei, etal. CRACK GROWTH PREDICTION AND FATIGUE DAMAGEEVALUATION ON WIN D TURBINE BLADEACTA ENERGIAE[J]. SOLARIS SINICA,2015,36(1):41-48.
[7] 张俊红,杨硕,刘海等. 裂纹参数对航空发动机叶片频率转向特性影响研究[J]. 振动与冲击,2014,20 (33):7-11.
ZHANG Jun-hong,YANG Shuo,LIU Hai,et al. Influence of crack parameters on frequency veering characteristic of aero-engine blade[J].Journal of Vibration and Shock,2015,20 (33):7-11.
[8] Chulwoo Jung, Akira Saito, Bogdan I.Epureanu. Detection of Cracks in Mistuned Bladed Disks Using Reduced-Order Models and Vibration Data[J]. Journal of Vibration and Acoustics, 2012, 134(12):061010.
[9] Akira Saito, Matthew P. Castanier, Christophe Pierre. Effects of a Cracked Blade on Mistuned Turbine Engine Rotor Vibration[J]. Journal of Vibration and Acoustics, 2009, 131(6): 061006.
[10] Sidra Khanam, J. K. Dutt, N. Tandon. Extracting Rolling Element Bearing Faults From Noisy Vibration Signal Using Kalman Filter[J]. Journal of Vibration and Acoustics, 2014, 136(6):031008.
[11] Yi-Jui Chiu, Shyh-Chin Huang. The Influence of a Cracked Blade on Rotor’s Free Vibration[J]. Journal of Vibration and Acoustics, 2008, 130(10): 054502
[12] 古莹奎,承姿辛,朱繁泷. 基于主成分分析和支持向量机的滚动轴承故障特征融合分析[J]. 中国机械工程,2015,28(20):2778-2787.
Gu Yingkui, Cheng Zixin, Zhu Fanlong. Rolling Bearing Fault Feature Fusion Based on PCA and SVM[J]. China Mechanical Engineering, 2015,28(20):2778-2787.