Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings
Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as cen...
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MDPI AG
2025-06-01
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| author | Baoxiang Wang Guoqing Liu Jihai Dai Chuancang Ding |
| author_facet | Baoxiang Wang Guoqing Liu Jihai Dai Chuancang Ding |
| author_sort | Baoxiang Wang |
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| description | Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments. |
| format | Article |
| id | doaj-art-e4213bad26d740c18d0caeb6f96da35f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e4213bad26d740c18d0caeb6f96da35f2025-08-20T03:46:42ZengMDPI AGSensors1424-82202025-06-012511354210.3390/s25113542Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling BearingsBaoxiang Wang0Guoqing Liu1Jihai Dai2Chuancang Ding3School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Rail Transportation, Intelligent Urban Rail Engineering Research Center of Jiangsu Province, Soochow University, Suzhou 215131, ChinaAccurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments.https://www.mdpi.com/1424-8220/25/11/3542rolling bearingsimproved variational mode decompositionscale space representationmultipoint kurtosisfault diagnosisvibration analysis |
| spellingShingle | Baoxiang Wang Guoqing Liu Jihai Dai Chuancang Ding Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings Sensors rolling bearings improved variational mode decomposition scale space representation multipoint kurtosis fault diagnosis vibration analysis |
| title | Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings |
| title_full | Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings |
| title_fullStr | Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings |
| title_full_unstemmed | Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings |
| title_short | Improved Variational Mode Decomposition Based on Scale Space Representation for Fault Diagnosis of Rolling Bearings |
| title_sort | improved variational mode decomposition based on scale space representation for fault diagnosis of rolling bearings |
| topic | rolling bearings improved variational mode decomposition scale space representation multipoint kurtosis fault diagnosis vibration analysis |
| url | https://www.mdpi.com/1424-8220/25/11/3542 |
| work_keys_str_mv | AT baoxiangwang improvedvariationalmodedecompositionbasedonscalespacerepresentationforfaultdiagnosisofrollingbearings AT guoqingliu improvedvariationalmodedecompositionbasedonscalespacerepresentationforfaultdiagnosisofrollingbearings AT jihaidai improvedvariationalmodedecompositionbasedonscalespacerepresentationforfaultdiagnosisofrollingbearings AT chuancangding improvedvariationalmodedecompositionbasedonscalespacerepresentationforfaultdiagnosisofrollingbearings |