Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring
Electrostatic detection is a highly accurate way to monitor system performance failures at an early stage. However, due to the weak electrostatic signal, it can be easily interfered with under complex real-world conditions, leading to a reduction in its monitoring capability. During the electrostati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/13/4/178 |
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| author | Ruochen Liu Han Yin Jianzhong Sun Lanchun Zhang |
| author_facet | Ruochen Liu Han Yin Jianzhong Sun Lanchun Zhang |
| author_sort | Ruochen Liu |
| collection | DOAJ |
| description | Electrostatic detection is a highly accurate way to monitor system performance failures at an early stage. However, due to the weak electrostatic signal, it can be easily interfered with under complex real-world conditions, leading to a reduction in its monitoring capability. During the electrostatic monitoring of rolling bearings, noise can easily drown out the effective signal, making it difficult to extract fault characteristics. In order to solve this problem, a sparse representation based on cluster-contraction stagewise orthogonal matching pursuit (CcStOMP) is proposed to extract the fault features in the electrostatic signals of rolling bearings. The method adds a clustering contraction mechanism to the stagewise orthogonal matching pursuit (StOMP) algorithm, performs secondary filtering based on atom similarity clustering on the selected atoms in the atom search process, updates the support set, and finally solves the weights and updates the residuals, so as to reconstruct the original electrostatic signals and extract the fault feature components of rolling bearings. The method maintains fast convergence while analysing the extraction effect by comparing the measured signals of rolling bearing outer ring and bearing roller faults with the traditional StOMP algorithm, and the results show that the CcStOMP algorithm has obvious advantages in accurately extracting the fault features in the electrostatic monitoring signals of rolling bearings. |
| format | Article |
| id | doaj-art-4d43a68c38b94df2850ccc6fb1bd4a6b |
| institution | DOAJ |
| issn | 2075-4442 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj-art-4d43a68c38b94df2850ccc6fb1bd4a6b2025-08-20T03:13:58ZengMDPI AGLubricants2075-44422025-04-0113417810.3390/lubricants13040178Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic MonitoringRuochen Liu0Han Yin1Jianzhong Sun2Lanchun Zhang3School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaElectrostatic detection is a highly accurate way to monitor system performance failures at an early stage. However, due to the weak electrostatic signal, it can be easily interfered with under complex real-world conditions, leading to a reduction in its monitoring capability. During the electrostatic monitoring of rolling bearings, noise can easily drown out the effective signal, making it difficult to extract fault characteristics. In order to solve this problem, a sparse representation based on cluster-contraction stagewise orthogonal matching pursuit (CcStOMP) is proposed to extract the fault features in the electrostatic signals of rolling bearings. The method adds a clustering contraction mechanism to the stagewise orthogonal matching pursuit (StOMP) algorithm, performs secondary filtering based on atom similarity clustering on the selected atoms in the atom search process, updates the support set, and finally solves the weights and updates the residuals, so as to reconstruct the original electrostatic signals and extract the fault feature components of rolling bearings. The method maintains fast convergence while analysing the extraction effect by comparing the measured signals of rolling bearing outer ring and bearing roller faults with the traditional StOMP algorithm, and the results show that the CcStOMP algorithm has obvious advantages in accurately extracting the fault features in the electrostatic monitoring signals of rolling bearings.https://www.mdpi.com/2075-4442/13/4/178electrostatic monitoringrolling bearingssparse representationfault diagnosisCcStOMP |
| spellingShingle | Ruochen Liu Han Yin Jianzhong Sun Lanchun Zhang Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring Lubricants electrostatic monitoring rolling bearings sparse representation fault diagnosis CcStOMP |
| title | Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring |
| title_full | Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring |
| title_fullStr | Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring |
| title_full_unstemmed | Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring |
| title_short | Research on Feature Extraction Method and Process Optimization of Rolling Bearing Faults Based on Electrostatic Monitoring |
| title_sort | research on feature extraction method and process optimization of rolling bearing faults based on electrostatic monitoring |
| topic | electrostatic monitoring rolling bearings sparse representation fault diagnosis CcStOMP |
| url | https://www.mdpi.com/2075-4442/13/4/178 |
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