Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition
This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed ba...
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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/10.1155/2020/6216903 |
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| _version_ | 1841524485439619072 |
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| author | Ying Zhang Anchen Wang |
| author_facet | Ying Zhang Anchen Wang |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications. |
| format | Article |
| id | doaj-art-c004ffbc3d3a480e8e044cfe4d2404ef |
| 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-c004ffbc3d3a480e8e044cfe4d2404ef2025-02-03T05:53:09ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/62169036216903Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF AcquisitionYing Zhang0Anchen Wang1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaThis paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications.http://dx.doi.org/10.1155/2020/6216903 |
| spellingShingle | Ying Zhang Anchen Wang Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition Shock and Vibration |
| title | Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition |
| title_full | Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition |
| title_fullStr | Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition |
| title_full_unstemmed | Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition |
| title_short | Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition |
| title_sort | research on the fault diagnosis method for rolling bearings based on improved vmd and automatic imf acquisition |
| url | http://dx.doi.org/10.1155/2020/6216903 |
| work_keys_str_mv | AT yingzhang researchonthefaultdiagnosismethodforrollingbearingsbasedonimprovedvmdandautomaticimfacquisition AT anchenwang researchonthefaultdiagnosismethodforrollingbearingsbasedonimprovedvmdandautomaticimfacquisition |