Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising
Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/3151802 |
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| _version_ | 1832552411660550144 |
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| author | Wentao He Cancan Yi Yourong Li Han Xiao |
| author_facet | Wentao He Cancan Yi Yourong Li Han Xiao |
| author_sort | Wentao He |
| collection | DOAJ |
| description | Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose. |
| format | Article |
| id | doaj-art-117aa81f7a424818b6f9ec4c4dfc3176 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-117aa81f7a424818b6f9ec4c4dfc31762025-02-03T05:58:43ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/31518023151802Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation DenoisingWentao He0Cancan Yi1Yourong Li2Han Xiao3School of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaWavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.http://dx.doi.org/10.1155/2016/3151802 |
| spellingShingle | Wentao He Cancan Yi Yourong Li Han Xiao Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising Shock and Vibration |
| title | Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising |
| title_full | Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising |
| title_fullStr | Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising |
| title_full_unstemmed | Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising |
| title_short | Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising |
| title_sort | research on mechanical fault diagnosis scheme based on improved wavelet total variation denoising |
| url | http://dx.doi.org/10.1155/2016/3151802 |
| work_keys_str_mv | AT wentaohe researchonmechanicalfaultdiagnosisschemebasedonimprovedwavelettotalvariationdenoising AT cancanyi researchonmechanicalfaultdiagnosisschemebasedonimprovedwavelettotalvariationdenoising AT yourongli researchonmechanicalfaultdiagnosisschemebasedonimprovedwavelettotalvariationdenoising AT hanxiao researchonmechanicalfaultdiagnosisschemebasedonimprovedwavelettotalvariationdenoising |