An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to sel...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/4973941 |
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| _version_ | 1832568123811692544 |
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| author | Zhicheng Qiao Yongqiang Liu Yingying Liao |
| author_facet | Zhicheng Qiao Yongqiang Liu Yingying Liao |
| author_sort | Zhicheng Qiao |
| collection | DOAJ |
| description | When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM). |
| format | Article |
| id | doaj-art-0ac62fb9b2dc42e78c23f73381fe8d20 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-0ac62fb9b2dc42e78c23f73381fe8d202025-02-03T00:59:42ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/49739414973941An Improved Method of EWT and Its Application in Rolling Bearings Fault DiagnosisZhicheng Qiao0Yongqiang Liu1Yingying Liao2School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaState Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang 050043, ChinaWhen the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).http://dx.doi.org/10.1155/2020/4973941 |
| spellingShingle | Zhicheng Qiao Yongqiang Liu Yingying Liao An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis Shock and Vibration |
| title | An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis |
| title_full | An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis |
| title_fullStr | An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis |
| title_full_unstemmed | An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis |
| title_short | An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis |
| title_sort | improved method of ewt and its application in rolling bearings fault diagnosis |
| url | http://dx.doi.org/10.1155/2020/4973941 |
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