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Research on fault feature extraction method of rolling bearing based on NMD and wavelet threshold denoising

Research on fault feature extraction method of rolling bearing based on NMD and wavelet threshold denoising
Research on fault feature extraction method of rolling bearing based on NMD and wavelet threshold denoising

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.

1070-9622
Xiao, Maohua
70e12f26-5ece-46cd-a186-3cac70e99a6f
Wen, Kai
1fab0b35-ffc2-486c-9980-ec55b8428255
Zhang, Cunyi
01ef5ebe-8f39-46e3-90a5-84fe468153f4
Zhao, Xiao
70e12f26-5ece-46cd-a186-3cac70e99a6f
Wei, Weihua
e3711fd6-9093-44b7-9534-70d8ae321ce3
Wu, Dan
febbf54b-9a0b-4f70-9c97-8f13f1e40972
Xiao, Maohua
70e12f26-5ece-46cd-a186-3cac70e99a6f
Wen, Kai
1fab0b35-ffc2-486c-9980-ec55b8428255
Zhang, Cunyi
01ef5ebe-8f39-46e3-90a5-84fe468153f4
Zhao, Xiao
70e12f26-5ece-46cd-a186-3cac70e99a6f
Wei, Weihua
e3711fd6-9093-44b7-9534-70d8ae321ce3
Wu, Dan
febbf54b-9a0b-4f70-9c97-8f13f1e40972

Xiao, Maohua, Wen, Kai, Zhang, Cunyi, Zhao, Xiao, Wei, Weihua and Wu, Dan (2018) Research on fault feature extraction method of rolling bearing based on NMD and wavelet threshold denoising. Shock and Vibration, 2018, [9495265]. (doi:10.1155/2018/9495265).

Record type: Article

Abstract

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.

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9495265 - Version of Record
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More information

Accepted/In Press date: 3 July 2018
e-pub ahead of print date: 19 August 2018

Identifiers

Local EPrints ID: 423820
URI: http://eprints.soton.ac.uk/id/eprint/423820
ISSN: 1070-9622
PURE UUID: ba956c19-9d3b-46c1-baf1-a91361c555ab
ORCID for Maohua Xiao: ORCID iD orcid.org/0000-0002-9714-3176
ORCID for Xiao Zhao: ORCID iD orcid.org/0000-0002-9714-3176

Catalogue record

Date deposited: 02 Oct 2018 16:30
Last modified: 17 Dec 2019 01:27

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Contributors

Author: Maohua Xiao ORCID iD
Author: Kai Wen
Author: Cunyi Zhang
Author: Xiao Zhao ORCID iD
Author: Weihua Wei
Author: Dan Wu

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