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...
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| Published in | IEEE access Vol. 6; pp. 35042 - 35056 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhao, Huimin Deng, Wu Yang, Xinhua Zhang, Shengjie |
| Author_xml | – sequence: 1 givenname: Wu orcidid: 0000-0002-6524-6760 surname: Deng fullname: Deng, Wu organization: Software Institute, Dalian Jiaotong University, Dalian, China – sequence: 2 givenname: Shengjie surname: Zhang fullname: Zhang, Shengjie organization: School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian, China – sequence: 3 givenname: Huimin surname: Zhao fullname: Zhao, Huimin email: hm_zhao1977@126.com organization: Software Institute, Dalian Jiaotong University, Dalian, China – sequence: 4 givenname: Xinhua surname: Yang fullname: Yang, Xinhua organization: Software Institute, Dalian Jiaotong University, Dalian, China |
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| Snippet | Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration... |
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| SubjectTerms | AM-FM components Complexity Empirical analysis empirical wavelet transform Entropy Fault diagnosis Feature extraction Fourier spectrum segmentation fuzzy entropy Hilbert transformation Motor bearing Pattern recognition Signal analysis Signal processing support vector machine Support vector machines Transmissions (machine elements) Vibration analysis Vibration measurement Vibrations Wavelet transforms |
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| Title | A Novel Fault Diagnosis Method Based on Integrating Empirical Wavelet Transform and Fuzzy Entropy for Motor Bearing |
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