Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method
In order to effectively extract the characteristics of nonstationary vibration signals from hydropower units under noise interference, an adaptive stochastic resonance and Fourier decomposition method (FDM) based on genetic algorithm (GA) are proposed in this paper. Firstly, GA is used to optimize t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6640040 |
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| _version_ | 1832554066784288768 |
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| author | Yan Ren Jin Huang Lei-Ming Hu Hong-Ping Chen Xiao-Kai Li |
| author_facet | Yan Ren Jin Huang Lei-Ming Hu Hong-Ping Chen Xiao-Kai Li |
| author_sort | Yan Ren |
| collection | DOAJ |
| description | In order to effectively extract the characteristics of nonstationary vibration signals from hydropower units under noise interference, an adaptive stochastic resonance and Fourier decomposition method (FDM) based on genetic algorithm (GA) are proposed in this paper. Firstly, GA is used to optimize the resonance parameters so that the signal can reach the optimal resonance and the signal-to-noise ratio (SNR) can be improved. Secondly, FDM is used to process the signal and the appropriate frequency band function is selected for reconstruction. Finally, Hilbert envelope demodulation analysis was performed on the reconstructed signal to obtain the fault characteristics from the envelope spectrum. In order to prove the effectiveness and superiority of the proposed method, comparative experiments are designed by using the simulated signal and the measured swing signal of a hydropower unit. The results show that this method can effectively remove the noise interference and improve the SNR and extract the characteristic frequency of the signal, which has the extensive engineering application value to the fault diagnosis of hydropower units. |
| format | Article |
| id | doaj-art-1e75dbd6735042179e018493036b8d5c |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-1e75dbd6735042179e018493036b8d5c2025-02-03T05:52:30ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66400406640040Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition MethodYan Ren0Jin Huang1Lei-Ming Hu2Hong-Ping Chen3Xiao-Kai Li4School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaJiang Xi Hong Ping Pumped Storage Company Limited, Nanchang 330000, ChinaState Grid Hunan Electric Power Company Limited, Changsha 410004, ChinaState Grid Hunan Electric Power Company Limited, Changsha 410004, ChinaIn order to effectively extract the characteristics of nonstationary vibration signals from hydropower units under noise interference, an adaptive stochastic resonance and Fourier decomposition method (FDM) based on genetic algorithm (GA) are proposed in this paper. Firstly, GA is used to optimize the resonance parameters so that the signal can reach the optimal resonance and the signal-to-noise ratio (SNR) can be improved. Secondly, FDM is used to process the signal and the appropriate frequency band function is selected for reconstruction. Finally, Hilbert envelope demodulation analysis was performed on the reconstructed signal to obtain the fault characteristics from the envelope spectrum. In order to prove the effectiveness and superiority of the proposed method, comparative experiments are designed by using the simulated signal and the measured swing signal of a hydropower unit. The results show that this method can effectively remove the noise interference and improve the SNR and extract the characteristic frequency of the signal, which has the extensive engineering application value to the fault diagnosis of hydropower units.http://dx.doi.org/10.1155/2021/6640040 |
| spellingShingle | Yan Ren Jin Huang Lei-Ming Hu Hong-Ping Chen Xiao-Kai Li Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method Shock and Vibration |
| title | Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method |
| title_full | Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method |
| title_fullStr | Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method |
| title_full_unstemmed | Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method |
| title_short | Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method |
| title_sort | research on fault feature extraction of hydropower units based on adaptive stochastic resonance and fourier decomposition method |
| url | http://dx.doi.org/10.1155/2021/6640040 |
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