Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing
The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bea...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-12-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9121 |
| _version_ | 1818657193318154240 |
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| author | Zhanglei Jiang Yapeng Wu Jun Li Yaru Liu Jifang Wang Xiaoli Xu |
| author_facet | Zhanglei Jiang Yapeng Wu Jun Li Yaru Liu Jifang Wang Xiaoli Xu |
| author_sort | Zhanglei Jiang |
| collection | DOAJ |
| description | The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved. |
| first_indexed | 2024-12-17T03:37:35Z |
| format | Article |
| id | doaj.art-ff349a141f06422987089f6394a4c9a9 |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-12-17T03:37:35Z |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-ff349a141f06422987089f6394a4c9a92022-12-21T22:05:06ZengWileyThe Journal of Engineering2051-33052019-12-0110.1049/joe.2018.9121JOE.2018.9121Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearingZhanglei Jiang0Yapeng Wu1Jun Li2Yaru Liu3Jifang Wang4Xiaoli Xu5The Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology UniversityThe Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology UniversityInner Monolia First machinery Group Co., LTDBaotou Service Management Vocational SchoolThe Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology UniversityThe Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology UniversityThe original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9121feature extractionmachine bearingshilbert transformsmechanical engineering computingrolling bearingsfault diagnosisvibrationssignal processingouter ringsbearing inner ringsactual rolling bearingsfault characteristic frequencyhilbert envelope signalhigh-frequency componentsfault informationphase coupling componentsbearing faultrolling bearingfault feature extraction1.5-dimensional spectrumemd |
| spellingShingle | Zhanglei Jiang Yapeng Wu Jun Li Yaru Liu Jifang Wang Xiaoli Xu Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing The Journal of Engineering feature extraction machine bearings hilbert transforms mechanical engineering computing rolling bearings fault diagnosis vibrations signal processing outer rings bearing inner rings actual rolling bearings fault characteristic frequency hilbert envelope signal high-frequency components fault information phase coupling components bearing fault rolling bearing fault feature extraction 1.5-dimensional spectrum emd |
| title | Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing |
| title_full | Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing |
| title_fullStr | Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing |
| title_full_unstemmed | Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing |
| title_short | Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing |
| title_sort | application of emd and 1 5 dimensional spectrum in fault feature extraction of rolling bearing |
| topic | feature extraction machine bearings hilbert transforms mechanical engineering computing rolling bearings fault diagnosis vibrations signal processing outer rings bearing inner rings actual rolling bearings fault characteristic frequency hilbert envelope signal high-frequency components fault information phase coupling components bearing fault rolling bearing fault feature extraction 1.5-dimensional spectrum emd |
| url | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9121 |
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