The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value
Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when be...
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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/10.1155/2016/5957179 |
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| _version_ | 1832564748988710912 |
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| author | Te Han Dongxiang Jiang Nanfei Wang |
| author_facet | Te Han Dongxiang Jiang Nanfei Wang |
| author_sort | Te Han |
| collection | DOAJ |
| description | Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise. |
| format | Article |
| id | doaj-art-50863307d39a470b8f7b3190448e0063 |
| 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-50863307d39a470b8f7b3190448e00632025-02-03T01:10:12ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/59571795957179The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular ValueTe Han0Dongxiang Jiang1Nanfei Wang2State Key Lab of Control and Simulation of Power System and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, ChinaState Key Lab of Control and Simulation of Power System and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, ChinaState Key Lab of Control and Simulation of Power System and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, ChinaNowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.http://dx.doi.org/10.1155/2016/5957179 |
| spellingShingle | Te Han Dongxiang Jiang Nanfei Wang The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value Shock and Vibration |
| title | The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value |
| title_full | The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value |
| title_fullStr | The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value |
| title_full_unstemmed | The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value |
| title_short | The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value |
| title_sort | fault feature extraction of rolling bearing based on emd and difference spectrum of singular value |
| url | http://dx.doi.org/10.1155/2016/5957179 |
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