The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation
Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault f...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/429185 |
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doaj-24b9ee868d28409080170eaa39ecab062020-11-24T22:34:17ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/429185429185The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope DemodulationJun Ma0Jiande Wu1Yugang Fan2Xiaodong Wang3Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaSince the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.http://dx.doi.org/10.1155/2015/429185 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun Ma Jiande Wu Yugang Fan Xiaodong Wang |
spellingShingle |
Jun Ma Jiande Wu Yugang Fan Xiaodong Wang The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation Mathematical Problems in Engineering |
author_facet |
Jun Ma Jiande Wu Yugang Fan Xiaodong Wang |
author_sort |
Jun Ma |
title |
The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation |
title_short |
The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation |
title_full |
The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation |
title_fullStr |
The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation |
title_full_unstemmed |
The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation |
title_sort |
rolling bearing fault feature extraction based on the lmd and envelope demodulation |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults. |
url |
http://dx.doi.org/10.1155/2015/429185 |
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