Compound fault diagnosis based on two-stage adaptive wavecluster

被引：0
|
作者：
Zuo H. [1 ,2 ]
Liu X. [1 ,2 ]
Hong L. [1 ,2 ]
机构：
[1] School of Mechanical and Electrical Engineering, Nanjing University Of Aeronautics And Astronautics, Nanjing
[2] School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2017年 / 23卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; Feature extraction; Two-stage adaptive; Wavecluster;
D O I：
10.13196/j.cims.2017.10.012
中图分类号：
学科分类号：
摘要：
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. © 2017, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：2180 / 2191
页数：11
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