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 fil...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/9495265 |
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| _version_ | 1832558034399789056 |
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| author | Maohua Xiao Kai Wen Cunyi Zhang Xiao Zhao Weihua Wei Dan Wu |
| author_facet | Maohua Xiao Kai Wen Cunyi Zhang Xiao Zhao Weihua Wei Dan Wu |
| author_sort | Maohua Xiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5c31e900f3924156b24fff34e59e551e |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5c31e900f3924156b24fff34e59e551e2025-02-03T01:33:27ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/94952659495265Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold DenoisingMaohua Xiao0Kai Wen1Cunyi Zhang2Xiao Zhao3Weihua Wei4Dan Wu5College of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaFaculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UKCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Engineering, Nanjing Agriculture University, Nanjing 210031, ChinaRolling 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.http://dx.doi.org/10.1155/2018/9495265 |
| spellingShingle | Maohua Xiao Kai Wen Cunyi Zhang Xiao Zhao Weihua Wei Dan Wu Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising Shock and Vibration |
| title | Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising |
| title_full | Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising |
| title_fullStr | Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising |
| title_full_unstemmed | Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising |
| title_short | Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising |
| title_sort | research on fault feature extraction method of rolling bearing based on nmd and wavelet threshold denoising |
| url | http://dx.doi.org/10.1155/2018/9495265 |
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