NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL
Some characteristics of bearing initial signals are considered including unobvious features, susceptibility to noise interference and strong nonlinearity when a bearing was damaged. Based on the fractal box dimension, an improved variational mode decomposition for nonlinear features extraction of be...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2022-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.01.006 |
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| _version_ | 1841535973412831232 |
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| author | JIN JiangTao XU ZiFei LI Chun MIAO WeiPao ZHANG WanFu LI Gen |
| author_facet | JIN JiangTao XU ZiFei LI Chun MIAO WeiPao ZHANG WanFu LI Gen |
| author_sort | JIN JiangTao |
| collection | DOAJ |
| description | Some characteristics of bearing initial signals are considered including unobvious features, susceptibility to noise interference and strong nonlinearity when a bearing was damaged. Based on the fractal box dimension, an improved variational mode decomposition for nonlinear features extraction of bearing fault signals（IVMD-NFE） is proposed. Because of the multi-measurement of nonlinear signals, the multifractal detrended fluctuation analysis（MF-DFA） method is used to study the multifractal characteristics of each fault signal. Taking the experimental data of rolling bearing as the research object, IVMD-NFE and MF-DFA method were used to analyze and diagnose the initial bearing signal. The results show that the signal extracted by the IVMD-NFE method can filter out noise to a greater extent and has lower fractal box dimension, and the extracted nonlinear characteristics are more representative; The fault signals of bearing exhibit multi-fractal features, the outer ring fault has the largest singularity index and strongest nonlinearity, and the cage fault is the smallest and weakest nonlinearity, indicating that the complexity of the data can better reflect the running state of the bearing. However, the method of using VMD or directly processing the original signal fails to extract effective nonlinear features, resulting in failure to distinguish faults. |
| format | Article |
| id | doaj-art-c7cacc270b954013bfac88d35c4b6dda |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-c7cacc270b954013bfac88d35c4b6dda2025-01-15T02:24:51ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692022-01-0144455229910336NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTALJIN JiangTaoXU ZiFeiLI ChunMIAO WeiPaoZHANG WanFuLI GenSome characteristics of bearing initial signals are considered including unobvious features, susceptibility to noise interference and strong nonlinearity when a bearing was damaged. Based on the fractal box dimension, an improved variational mode decomposition for nonlinear features extraction of bearing fault signals（IVMD-NFE） is proposed. Because of the multi-measurement of nonlinear signals, the multifractal detrended fluctuation analysis（MF-DFA） method is used to study the multifractal characteristics of each fault signal. Taking the experimental data of rolling bearing as the research object, IVMD-NFE and MF-DFA method were used to analyze and diagnose the initial bearing signal. The results show that the signal extracted by the IVMD-NFE method can filter out noise to a greater extent and has lower fractal box dimension, and the extracted nonlinear characteristics are more representative; The fault signals of bearing exhibit multi-fractal features, the outer ring fault has the largest singularity index and strongest nonlinearity, and the cage fault is the smallest and weakest nonlinearity, indicating that the complexity of the data can better reflect the running state of the bearing. However, the method of using VMD or directly processing the original signal fails to extract effective nonlinear features, resulting in failure to distinguish faults.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.01.006Variational mode decompositionFractalBearingNonlinearFault diagnosis |
| spellingShingle | JIN JiangTao XU ZiFei LI Chun MIAO WeiPao ZHANG WanFu LI Gen NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL Jixie qiangdu Variational mode decomposition Fractal Bearing Nonlinear Fault diagnosis |
| title | NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL |
| title_full | NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL |
| title_fullStr | NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL |
| title_full_unstemmed | NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL |
| title_short | NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL |
| title_sort | nonlinear analysis of bearing signal based on improved variational modal decomposition and muti fractal |
| topic | Variational mode decomposition Fractal Bearing Nonlinear Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.01.006 |
| work_keys_str_mv | AT jinjiangtao nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal AT xuzifei nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal AT lichun nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal AT miaoweipao nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal AT zhangwanfu nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal AT ligen nonlinearanalysisofbearingsignalbasedonimprovedvariationalmodaldecompositionandmutifractal |