A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing
Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8356572/ |
| _version_ | 1819172062618451968 |
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| author | Wu Deng Shengjie Zhang Huimin Zhao Xinhua Yang |
| author_facet | Wu Deng Shengjie Zhang Huimin Zhao Xinhua Yang |
| author_sort | Wu Deng |
| collection | DOAJ |
| description | Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated-frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing. |
| first_indexed | 2024-12-22T20:01:13Z |
| format | Article |
| id | doaj.art-42d44e350133428dbe3d988dbf27006c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:01:13Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-42d44e350133428dbe3d988dbf27006c2022-12-21T18:14:17ZengIEEEIEEE Access2169-35362018-01-016350423505610.1109/ACCESS.2018.28345408356572A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor BearingWu Deng0https://orcid.org/0000-0002-6524-6760Shengjie Zhang1Huimin Zhao2Xinhua Yang3Software Institute, Dalian Jiaotong University, Dalian, ChinaSchool of Electronics and Information Engineering, Dalian Jiaotong University, Dalian, ChinaSoftware Institute, Dalian Jiaotong University, Dalian, ChinaSoftware Institute, Dalian Jiaotong University, Dalian, ChinaMotor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated-frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing.https://ieeexplore.ieee.org/document/8356572/Motor bearingfault diagnosisempirical wavelet transformfuzzy entropysupport vector machineFourier spectrum segmentation |
| spellingShingle | Wu Deng Shengjie Zhang Huimin Zhao Xinhua Yang A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing IEEE Access Motor bearing fault diagnosis empirical wavelet transform fuzzy entropy support vector machine Fourier spectrum segmentation |
| title | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
| title_full | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
| title_fullStr | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
| title_full_unstemmed | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
| title_short | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
| title_sort | novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing |
| topic | Motor bearing fault diagnosis empirical wavelet transform fuzzy entropy support vector machine Fourier spectrum segmentation |
| url | https://ieeexplore.ieee.org/document/8356572/ |
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