Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing
Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to r...
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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/10.1155/2020/8901794 |
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| _version_ | 1832558446772224000 |
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| author | Zhi Xu Gang Tang Mengfu He |
| author_facet | Zhi Xu Gang Tang Mengfu He |
| author_sort | Zhi Xu |
| collection | DOAJ |
| description | Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method. |
| format | Article |
| id | doaj-art-4d91b67024d34339a4b982ee7fcedd16 |
| 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-4d91b67024d34339a4b982ee7fcedd162025-02-03T01:32:23ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/89017948901794Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element BearingZhi Xu0Gang Tang1Mengfu He2State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, Shenzhen, Guangdong 518172, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaState Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, Shenzhen, Guangdong 518172, ChinaRolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method.http://dx.doi.org/10.1155/2020/8901794 |
| spellingShingle | Zhi Xu Gang Tang Mengfu He Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing Shock and Vibration |
| title | Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing |
| title_full | Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing |
| title_fullStr | Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing |
| title_full_unstemmed | Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing |
| title_short | Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing |
| title_sort | peak based mode decomposition for weak fault feature enhancement and detection of rolling element bearing |
| url | http://dx.doi.org/10.1155/2020/8901794 |
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