基于气动信号分析的风机叶片裂纹故障识别

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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。

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	黎少辉1 蔡利梅2

关键词 ：风机叶片裂纹；故障识别；气动信号；小波变换；主成分分析；K均值聚类

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．

Key words： fan blade crack fault diagnosis pneumatic signals wavelet transform principal components analysis K-means

收稿日期: 2016-01-08 出版日期: 2017-09-28

引用本文:

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. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(19): 227-231.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I19/227

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