关键词: 故障诊断, 小波聚类, 特征提取, 双阶自适应

Abstract: Feature extraction and fault diagnosis method are particularly important for diagnosing the faults of aero-engine rotor quickly and accurately.Aiming at the non-stationary and nonlinear characteristics of aero-engine's vibration signals,the wavelet transform and Hilbert-Huang Transform (HHT) method were used to extract the three feature vectors that were the effective value of signal,the marginal spectrum centroid and the power spectral centroid of maximum energy level which was obtained with wavelet transform.The two-stage adaptive wavecluster was applied to diagnose the mixed fault of aero-engine rotor.The process of two-stage adaptive wavecluster was that: the large grid cell was used to quantify the data space,and the area of clustering was found to achieve presorting clustering of data;the sub cluster region was subdivided adaptively again,and wavecluster was implemented in this region;compared with the fault sample,the fault type of cluster was identified.The results showed that the proposed method could achieve fault classification and recognition quickly and accurately.Especially for the mixed multi-type data with non-uniform density,the diagnostic accuracy was significantly higher than the traditional wavecluster.

Key words: fault diagnosis, wavecluster, feature extraction, two-stage adaptive