Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/18/3706 |
| _version_ | 1818327659740921856 |
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| author | Xiaolong Wang Xiaoli Yan Yuling He |
| author_facet | Xiaolong Wang Xiaoli Yan Yuling He |
| author_sort | Xiaolong Wang |
| collection | DOAJ |
| description | The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement. |
| first_indexed | 2024-12-13T12:19:47Z |
| format | Article |
| id | doaj.art-77528d5a19a34f7384f2255a833a2adc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T12:19:47Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-77528d5a19a34f7384f2255a833a2adc2022-12-21T23:46:36ZengMDPI AGApplied Sciences2076-34172019-09-01918370610.3390/app9183706app9183706Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDDXiaolong Wang0Xiaoli Yan1Yuling He2Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaThe fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.https://www.mdpi.com/2076-3417/9/18/3706optimized cyclostationary blind deconvolutionfrequency weighted energy entropysingular value decomposition denoisingwind turbine bearingweak feature enhancement |
| spellingShingle | Xiaolong Wang Xiaoli Yan Yuling He Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD Applied Sciences optimized cyclostationary blind deconvolution frequency weighted energy entropy singular value decomposition denoising wind turbine bearing weak feature enhancement |
| title | Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD |
| title_full | Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD |
| title_fullStr | Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD |
| title_full_unstemmed | Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD |
| title_short | Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD |
| title_sort | weak fault feature extraction and enhancement of wind turbine bearing based on ocycbd and svdd |
| topic | optimized cyclostationary blind deconvolution frequency weighted energy entropy singular value decomposition denoising wind turbine bearing weak feature enhancement |
| url | https://www.mdpi.com/2076-3417/9/18/3706 |
| work_keys_str_mv | AT xiaolongwang weakfaultfeatureextractionandenhancementofwindturbinebearingbasedonocycbdandsvdd AT xiaoliyan weakfaultfeatureextractionandenhancementofwindturbinebearingbasedonocycbdandsvdd AT yulinghe weakfaultfeatureextractionandenhancementofwindturbinebearingbasedonocycbdandsvdd |