Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD
An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best str...
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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/10.1155/2016/9278581 |
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| _version_ | 1832564455734509568 |
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| author | Peiming Shi Cuijiao Su Dongying Han |
| author_facet | Peiming Shi Cuijiao Su Dongying Han |
| author_sort | Peiming Shi |
| collection | DOAJ |
| description | An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect. |
| format | Article |
| id | doaj-art-264aa99cd8444e2c87cee2c0d8854b67 |
| 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-264aa99cd8444e2c87cee2c0d8854b672025-02-03T01:10:51ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/92785819278581Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMDPeiming Shi0Cuijiao Su1Dongying Han2Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, ChinaInstitute of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, ChinaAn adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.http://dx.doi.org/10.1155/2016/9278581 |
| spellingShingle | Peiming Shi Cuijiao Su Dongying Han Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD Shock and Vibration |
| title | Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD |
| title_full | Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD |
| title_fullStr | Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD |
| title_full_unstemmed | Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD |
| title_short | Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD |
| title_sort | fault diagnosis of rotating machinery based on adaptive stochastic resonance and amd eemd |
| url | http://dx.doi.org/10.1155/2016/9278581 |
| work_keys_str_mv | AT peimingshi faultdiagnosisofrotatingmachinerybasedonadaptivestochasticresonanceandamdeemd AT cuijiaosu faultdiagnosisofrotatingmachinerybasedonadaptivestochasticresonanceandamdeemd AT dongyinghan faultdiagnosisofrotatingmachinerybasedonadaptivestochasticresonanceandamdeemd |