Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks
Aiming at the characteristics of weak vibration signal, strong interference, unevenness and nonlinearity in rolling bearing faults, this paper proposes a bearing fault intelligent diagnosis method based on variational modal decomposition (VMD) and Multisynchrosqueezing transform (MSST). First, the o...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9780385/ |
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| author | Tao Liang Qingzhao Lv Jianxin Tan Yanwei Jing Hexu Sun |
| author_facet | Tao Liang Qingzhao Lv Jianxin Tan Yanwei Jing Hexu Sun |
| author_sort | Tao Liang |
| collection | DOAJ |
| description | Aiming at the characteristics of weak vibration signal, strong interference, unevenness and nonlinearity in rolling bearing faults, this paper proposes a bearing fault intelligent diagnosis method based on variational modal decomposition (VMD) and Multisynchrosqueezing transform (MSST). First, the original vibration signal of the bearing is decomposed into multiple intrinsic mode functions (IMF) through the optimal parameter VMD, and then an effective IMF is selected for signal reconstruction according to the kurtosis value and mutual information. Secondly, MSST is applied to the reconstructed signal to obtain a time-frequency (TF) images with high energy concentration, and then extract the time-frequency and time-amplitude signals of the vibration signal in the TF images according to the ridge detection algorithm. Finally, these time series are used as input, using Long short-term memory (LSTM) network is trained to complete the intelligent classification and diagnosis of bearing failure. This method is compared with other fault diagnosis methods through test data sets. The results show that the method proposed in this paper is superior to other methods in recognition accuracy and recognition type, can accurately identify and classify bearing faults, and has strong generalization. Ability to better meet the needs of actual engineering. |
| format | Article |
| id | doaj-art-f3e31141bfc14648a8ac900e101da2bb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f3e31141bfc14648a8ac900e101da2bb2025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312068412069510.1109/ACCESS.2022.31776509780385Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory NetworksTao Liang0Qingzhao Lv1Jianxin Tan2Yanwei Jing3Hexu Sun4https://orcid.org/0000-0002-2499-7867School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, ChinaSchool of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, ChinaHebei Jiantou Energy Investment Company Ltd., Shijiazhuang, ChinaXintian Green Energy Company Ltd., Shijiazhuang, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, ChinaAiming at the characteristics of weak vibration signal, strong interference, unevenness and nonlinearity in rolling bearing faults, this paper proposes a bearing fault intelligent diagnosis method based on variational modal decomposition (VMD) and Multisynchrosqueezing transform (MSST). First, the original vibration signal of the bearing is decomposed into multiple intrinsic mode functions (IMF) through the optimal parameter VMD, and then an effective IMF is selected for signal reconstruction according to the kurtosis value and mutual information. Secondly, MSST is applied to the reconstructed signal to obtain a time-frequency (TF) images with high energy concentration, and then extract the time-frequency and time-amplitude signals of the vibration signal in the TF images according to the ridge detection algorithm. Finally, these time series are used as input, using Long short-term memory (LSTM) network is trained to complete the intelligent classification and diagnosis of bearing failure. This method is compared with other fault diagnosis methods through test data sets. The results show that the method proposed in this paper is superior to other methods in recognition accuracy and recognition type, can accurately identify and classify bearing faults, and has strong generalization. Ability to better meet the needs of actual engineering.https://ieeexplore.ieee.org/document/9780385/Bearing fault diagnosisvariational modal decompositionmultisynchrosqueezing transformlong short-term memory |
| spellingShingle | Tao Liang Qingzhao Lv Jianxin Tan Yanwei Jing Hexu Sun Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks IEEE Access Bearing fault diagnosis variational modal decomposition multisynchrosqueezing transform long short-term memory |
| title | Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks |
| title_full | Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks |
| title_fullStr | Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks |
| title_full_unstemmed | Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks |
| title_short | Fault Diagnosis Method of Rolling Bearing Based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined With Long Short-Term Memory Networks |
| title_sort | fault diagnosis method of rolling bearing based on variational modal decomposition multisynchrosqueezing transform combined with long short term memory networks |
| topic | Bearing fault diagnosis variational modal decomposition multisynchrosqueezing transform long short-term memory |
| url | https://ieeexplore.ieee.org/document/9780385/ |
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