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malan018: 金币+10, ★★★★★最佳答案 2013-06-19 15:15:16
Accession number:
20132116362862
Title:
Research on feature extraction of rolling bearing incipient fault based on Morlet wavelet transform
Authors:
Ma, Lun1 ; Kang, Jianshe1 ; Meng, Yan2; Lv, Lei3
Author affiliation:
1Department of Equipment Command and Management, Ordnance Engineering College, Shijiazhuang 050003, China
2Hebei Electric Power Design and Research Institute, Shijiazhuang 050003, China
362191 Troops of PLA, Huaxian 714100, China
Corresponding author:
Ma, L. (malun018@163.com)
Source title:
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
Abbreviated source title:
Yi Qi Yi Biao Xue Bao
Volume:
34
Issue:
4
Issue date:
April 2013
Publication year:
2013
Pages:
920-926
Language:
Chinese
ISSN:
02543087
CODEN:
YYXUDY
Document type:
Journal article (JA)
Publisher:
Science Press, 18,Shuangqing Street,Haidian, Beijing, 100085, China
Abstract:
Aiming at the problem that at early stage of bearing fault, the feature components of the original vibration data are easy to be submerged in noise signal and can not be detected in time. According to the de-noising principle of Morlet wavelet transform, a method is presented, which determines the optimal scale parameter based on scale related power distribution, so that the signal is filtered under this scale and the impact feature components are extracted. The main filtering procedures include: the minimum Shannon entropy is used to optimize the Morlet wave shape factor, the best match between mother wavelet and signal fault feature is realized; the scale-power spectrum is plotted with the wavelet transform coefficients of the best Morlet continuous wavelet under different transform scales; and according to the accumulative characteristic of the fault feature power in specific scale range, the scale parameter with best filtering effect is selected from the extreme points in the scale power spectrum. The actual processing result for bearing full lifetime vibration datasets shows that the proposed method can extract the weak feature components and detect the existence of related outer-race fault in advance compared with root-mean-square trend. So this method can be regarded as an effective approach for diagnosing bearing incipient fault.
Number of references:
16
Main heading:
Wavelet transforms
Controlled terms:
Bearings (machine parts) - Feature extraction - Optimization - Power spectrum - Roller bearings
Uncontrolled terms:
Incipient fault feature extraction - Morlet wavelet transform - Rolling bearings - Scale-power spectrum - Shannon entropy
Classification code:
601.2 Machine Components - 711 Electromagnetic Waves - 716 Telecommunication; Radar, Radio and Television - 921 Mathematics - 941 Acoustical and Optical Measuring Instruments
Database:
Compendex
Compilation and indexing terms, © 2013 Elsevier Inc. |
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