Fault Feature Extraction of Rotating Machinery Based on Wavelet Transform and Self-organizing Map Network

被引：0

作者：

Gong, Maofa ^{[1
]}

Zhang, Xiaoming ^{[1
]}

Liu, Qingxue ^{[1
]}

Zhao, Zidong ^{[1
]}

Zhang, Xiaoli ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao, Shandong, Peoples R China

来源：

2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年

关键词：

Rotating Machinery; Discrete Wavelet Transform (DWT); Self-organizing Map Network; Feature Extraction;

D O I：

10.1109/WCICA.2010.5554545

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, it expounded in detail the principle of energy spectrum analysis based on Discrete Wavelet Transform and Multi-resolution Analysis. In the aspect of study on feature extraction method, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, this paper imported wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of solving the problem that fault feature symptom is described comprehensively.

引用

页码：5877 / 5880

页数：4

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