Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising
Joint Authors
Xiao, Maohua
Wei, Weihua
Wu, Dan
Wen, Kai
Zhang, Cunyi
Zhao, Xiao
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Rolling bearings are the core components of the machine.
In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field.
At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings.
As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method.
First of all, the fault signal was preprocessed via wavelet threshold denoising.
Then, the denoised signal was decomposed by using NMD.
Next, the mode component envelope spectrum was made.
Finally, the fault characteristic frequency of rolling bearing was extracted.
The method was compared with EMD through the simulation experiment and rolling bearing fault experiment.
Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.
American Psychological Association (APA)
Xiao, Maohua& Wen, Kai& Zhang, Cunyi& Zhao, Xiao& Wei, Weihua& Wu, Dan. 2018. Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising. Shock and Vibration،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1215556
Modern Language Association (MLA)
Xiao, Maohua…[et al.]. Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising. Shock and Vibration No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1215556
American Medical Association (AMA)
Xiao, Maohua& Wen, Kai& Zhang, Cunyi& Zhao, Xiao& Wei, Weihua& Wu, Dan. Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1215556
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215556