Bearing Fault Diagnosis Based on EMD and PSD

被引：8
|
作者：
Huang, Ping [1 ]
Pan, Ziwei [1 ]
Qi, Xiaoli [1 ]
Lei, Jiapeng [1 ]
机构：
[1] Anhui Univ Technol, Sch Mech Engn, Maanshan 243002, Anhui, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词：
mode decomposition; intrinsic mode function; power spectral density; fault diagnosis; roller bearing; EMPIRICAL MODE DECOMPOSITION; HUANG SPECTRAL-ANALYSIS; SYSTEM-IDENTIFICATION; HILBERT;
D O I：
10.1109/WCICA.2010.5554896
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new method which combines empirical mode decomposition (EMD) and power spectral density (PSD) together for bearing fault diagnosis in low speed-high load rotary machine. EMD is a novel self-adaptive method which is based on partial characters of the signal. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Then calculate the PSD of each IMF. The results of application in simulation signal and practical bearing fault signal both show its efficiency.
引用
收藏
页码：1300 / 1304
页数：5
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